SYSTEM AND METHOD FOR INTEGRATED DISASTER MANAGEMENT OPERATIONS USING GENERATIVE AI AND MACHINE LEARNING

Abstract

Embodiments of this disclosure relate to an integrated system for disaster management operations comprising a Unified Disaster Management Control Module operably connected to computing devices and a server over a network. The system manages functional modules across pre-disaster, during-disaster, and post-disaster stages. These modules include a Vector Data Module for geo-tagged data collection, a Forecast Data Module for weather risk prediction, and a Real-Time Data Monitoring Module using IoT sensors and crowdsourced data. A Decision Management Tool uses AI/ML algorithms to generate disaster scenarios, while a Dissemination Module sends real-time alerts to responders and the public. The system also includes a Deployment Module for tracking resources, an Evacuation & Relief Operations Module for managing evacuation, and post-disaster modules like the Damages and Restoration Module and Enumeration & Compensation Module. The system integrates third-party tools, thereby optimizing disaster response, resource allocation, and continuous system improvement.

Specification

Description:TECHNICAL FIELD
[001] The disclosed subject matter relates generally to integrated disaster management systems. More particularly, the present invention pertains to a system and method for handling disaster management operations using Generative AI and Machine Learning technologies to efficiently manage and mitigate various types of disasters across pre-disaster, during-disaster, and post-disaster stages.

BACKGROUND
[002] Disaster management has traditionally been addressed through a combination of manual processes, independent monitoring systems, and basic communication tools. These systems, though functional to a certain degree, are often fragmented, reactive, and limited in scope. Over the years, several solutions have been implemented to manage disasters, yet they exhibit significant drawbacks that hinder their effectiveness, especially during rapidly evolving disaster situations.

[003] One of the primary approaches to disaster management has been the reliance on manual and fragmented systems. In many regions, disaster response teams still depend on paper-based systems, phone calls, and in-person meetings to coordinate response efforts. Although digital tools exist, they are often disconnected, with departments relying on isolated data sources. This leads to inefficiencies, as these methods are slow, prone to human error, and unsuitable for fast-moving disaster scenarios. The poor coordination between departments causes delays in response and a lack of unified situational awareness, which can significantly worsen the impact of disasters.

[004] Independent monitoring tools are another common solution, where agencies employ specialized systems like weather forecasting from the India Meteorological Department, GIS for asset mapping, and IoT-based real-time data monitoring through automatic weather stations and water gauges. While these tools provide critical information, they typically operate in silos, offering a narrow view limited to specific data types. The inability to integrate this data into a centralized platform means responders must juggle multiple systems, which hampers decision-making and diminishes the ability to comprehensively assess the disaster scenario.

[005] Basic early warning and communication systems, such as sirens, SMS alerts, and social media notifications, are used to notify the public about impending disasters. However, these systems are largely one-way, without the ability to collect or act on real-time feedback from the field. Additionally, communication between departments and agencies during emergencies is inconsistent, and infrastructure failures caused by disasters often render these communication tools ineffective.

[006] In the realm of weather forecasting, predictive models like the Global Forecast System (GFS), etc. are used to estimate risks associated with weather-related disasters, such as temperature, rainfall, floods or storms. While these models offer a degree of accuracy, they are generally limited to specific types of natural disasters and do not account for real-time changes in environmental conditions or human activity. Moreover, they fail to incorporate factors like infrastructure vulnerabilities or the availability of critical resources, which are essential for an effective disaster response.

[007] Some platforms focus on disaster response coordination by offering digital dashboards that track resources, visualize disaster data, and manage post-disaster recovery efforts. However, these platforms often become active only after the disaster has occurred, lacking the capability for proactive, real-time disaster management. Additionally, these systems frequently lack integration with IoT devices or AI-powered tools that could provide predictive insights and enhance the effectiveness of disaster response operations.
[008] The main drawbacks of these known solutions include a lack of integration, slow and reactive responses, inconsistent communication, limited predictive capabilities, and inadequate resource management. The lack of a unified system that integrates data from weather forecasting, real-time monitoring, and geographic information systems is a significant limitation. Current disaster management solutions tend to react only after a disaster has occurred, which delays response efforts, increases the potential for damage and loss of life, and often leads to inadequate recovery strategies.

[009] In contrast, the proposed system effectively functions during any type of disaster and manages disasters in 360 degrees, thereby addressing these limitations by providing a fully integrated, real-time, predictive disaster management platform. This system combines data collection, predictive modeling, automated decision-making, and seamless communication across departments in a single platform. The proposed system is proactive rather than reactive, enabling faster, more efficient, and more coordinated disaster responses. By leveraging advanced technologies like Generative AI and Machine Learning, the system refines and improves its responses based on real-time learnings and historical data.

[0010] In light of the existing problems and limitations, the proposed system offers a much-needed solution to the inefficiencies, coordination challenges, and delayed responses that plague current disaster management operations.

SUMMARY
[0011] The following invention presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[0012] The exemplary embodiments of the present disclosure pertain to a system and method for handling disaster management operations using Generative AI and Machine Learning technologies to efficiently manage and mitigate various types of disasters across pre-disaster, during-disaster, and post-disaster stages.

[0013] The objective of the present disclosure is to provide an integrated system that manages disaster operations in 360 degrees, offering a comprehensive and proactive approach to disaster management. The proposed system leverages AI, IoT, and real-time data to optimize each stage of disaster response, from prediction to recovery.

[0014] Another objective of the present disclosure is to reduce response times by using real-time data processing and predictive analytics, enabling quicker decision-making and more efficient disaster responses. This automation eliminates delays common in manual systems and allows responders to act proactively.

[0015] Another objective of the present disclosure is to integrate multiple independent modules, such as vector data, real-time monitoring, and resource tracking, into a unified platform. This enhances coordination and provides a holistic view of the disaster situation, addressing the fragmentation seen in existing solutions.

[0016] Another objective of the present disclosure is to provide continuous real-time monitoring through IoT sensors, Satellite based Communication tools and GPS, offering up-to-date insights that improve situational awareness. This ensures more accurate decision-making in dynamic disaster environments.

[0017] Another objective of the present disclosure is to ensure effective resource allocation by tracking responders, vehicles, and equipment in real time, thereby optimizing the deployment of emergency resources and minimizing wastage.

[0018] Another objective of the present disclosure is to enhance communication through multi-channel disaster alerts, ensuring reliable dissemination of information across SMS, social media, and other platforms. The system's failsafe communication mechanism guarantees functionality even during infrastructure failures.

[0019] Another objective of the present disclosure is to incorporate automated decision-making using AI and machine learning, which analyzes vast amounts of data to generate disaster scenarios and recommend optimal response strategies, reducing reliance on human operators.

[0020] Another objective of the present disclosure is to provide a scalable and modular disaster management platform, allowing the system to adapt to various types of disasters, whether natural or man-made, ensuring flexibility in its deployment.

[0021] Another objective of the present disclosure is to support post-disaster recovery efforts by assessing damages in real time, tracking relief efforts, and facilitating compensation distribution, ensuring comprehensive disaster management from prediction to recovery.

[0022] In an exemplary embodiment of the present disclosure, the proposed system integrates advanced technologies such as Generative AI, Machine Learning, IoT, and GIS into a unified disaster management platform. This integration enables real-time data processing, predictive analytics, and automated decision-making to optimize disaster response and recovery efforts.

[0023] Another exemplary embodiment of the present disclosure, the system captures and stores geo-tagged vector data of critical assets using mobile applications and real-time monitoring tools. This geo-tagged data is integrated with forecast information from national and private sources to enhance predictive disaster modeling and scenario planning.

[0024] Another exemplary embodiment of the present disclosure, the system employs IoT-based sensors and crowdsourced information to continuously monitor environmental conditions such as weather and water levels. Real-time data collected is processed instantly, providing up-to-date insights that improve situational awareness during disasters.

[0025] Another exemplary embodiment of the present disclosure, the proposed system utilizes Generative AI and Machine Learning algorithms to analyze historical and real-time data. This analysis generates predictive models and offers scenario-based decision support, enabling proactive measures and reducing reliance on manual interpretation.

[0026] Another exemplary embodiment of the present disclosure, the system automatically disseminates disaster alerts through multiple communication channels, including SMS, social media, and television broadcasts. A failsafe communication mechanism ensures that alerts reach responders and the public even if standard communication infrastructure fails during a disaster.

[0027] Another exemplary embodiment of the present disclosure, the system includes tools for planning and tracking the deployment of emergency responders and resources. Using Satellite based Communication tools, GPS tracking and heatmaps, it visualizes the locations of personnel and equipment in real time, ensuring efficient resource allocation during evacuation and relief operations.
[0028] Another exemplary embodiment of the present disclosure, the proposed system features a modular design that allows independent modules to function autonomously or in an integrated manner based on the disaster type and real-time situation. This scalability and adaptability enable the system to manage various natural and man-made disasters effectively.

[0029] Another exemplary embodiment of the present disclosure, the system refines its predictive capabilities over time by learning from real-time incidents and adjusting the vector and forecast data accordingly. This continuous learning process enhances the system's ability to forecast and manage future disasters more effectively.

[0030] Another exemplary embodiment of the present disclosure, the system supports post-disaster recovery by assessing damages using real-time geotagging and mobile applications. It tracks relief efforts, monitors infrastructure restoration, and assists in compensation distribution, providing comprehensive management from disaster onset to recovery.

BRIEF DESCRIPTION OF THE DRAWINGS
[0031] Fig. 1 depicts the overall architecture of the system, showing the Unified Disaster Management Control Module connecting both the client-side and server-side over a network to manage disaster operations effectively.

[0032] Fig. 2 depicts the functional submodules of the Unified Disaster Management Control Module, outlining the key submodules on both the client-side (computing devices) and server-side (data processing).

[0033] Fig. 3 depicts the Vector Data Module on the client-side, which collects and integrates geo-tagged data through mobile applications, contributing to disaster analysis and planning.
[0034] Fig. 4 depicts the Real-Time Data Monitoring Module on the client-side, which integrates IoT sensors, GPS data, and crowdsourced information for continuous monitoring of disaster conditions.

[0035] Fig. 5 depicts the Evacuation & Relief Operations Module on the client-side, responsible for tracking and managing the movement of resources and evacuees during disaster relief operations.

[0036] Fig. 6 depicts the Deployment Module on the client-side, focusing on real-time tracking of responders, vehicles, and relief resources using Satellite based Communication tools , Mobile app, GPS and heatmap visualization.

[0037] Fig. 7 depicts the Damages & Restoration Module on the client-side, which handles the assessment of damages and the monitoring of restoration efforts through geotagging and real-time updates.

[0038] Fig. 8 depicts the Enumeration & Compensation Module on the client-side, which processes affected household data and determines eligibility for compensation through real-time mapping and status monitoring.

[0039] Fig. 9 depicts the Forecast Data Module on the server-side, which processes weather and environmental forecast data from various sources to support predictive disaster management.

[0040] Fig. 10 depicts the Decision Management Tool - Gen AI Module on the server-side, which leverages AI and ML technologies to provide scenario-based decision-making and real-time disaster response recommendations.

[0041] Fig. 11 depicts the Dissemination Module on the server-side, responsible for sending disaster alerts and notifications to the public and responders through multiple communication channels like SMS, social media, and email.

[0042] Fig. 12 depicts the Analytics, Reports & Presentation Module - AI Module on the server-side, which generates AI-driven reports and presentations based on disaster data, supporting post-disaster analysis and recovery planning.

[0043] Fig. 13 depicts the Vector Data Module in the disaster management system, which collects and integrates geo-tagged vector data from various sources such as state-owned GIS data, departmental assets, and private establishments. The data is stored in GeoJson format for further processing and visualization within the system.

[0044] Fig. 14 depicts the architecture of the Vector Data Module within the disaster management system. It illustrates the flow of state-owned GIS data, starting from mobile apps and web applications for geotagging, followed by data storage, query building using Java, and visualization. This module is designed to manage and visualize vector data for disaster analysis and resource mapping.

[0045] Fig. 15 depicts the Forecast Data Module within the disaster management system. It shows the integration of various forecast data sources, including governmental and private agencies, which provide weather, flood, and cyclone information. This module processes and visualizes forecast data to support proactive disaster response and management.

[0046] Fig. 16 depicts the architecture of the Forecast Data Module within the disaster management system. It shows the process of gathering forecast data from government and private sources via an FTP server, followed by processing using Python and Java to generate spatial distribution maps. This module handles and visualizes forecast data for disaster prediction and management.

[0047] Fig. 17 depicts the Real Time Data Monitoring Module within the disaster management system. It shows the collection of real-time data from sources such as IoT-based weather stations, GPS tracking devices, drones, and crowdsourced information. This module enables continuous monitoring of environmental and incident data for effective disaster response.

[0048] Fig. 18 depicts the architecture of the Real Time Data Monitoring Module within the disaster management system. It shows how data from IoT devices, GPS tracking, mobile apps, and crowdsourced information is gathered through APIs, processed, and monitored in real time. This module enables continuous incident monitoring and provides critical data for timely disaster response.

[0049] Fig. 19 depicts the Decision Management System - Gen AI Module within the disaster management system. It shows how data from real-time monitoring, forecast data, and vector data are processed by the AI-driven decision management system. This module generates automated alerts and provides decision support based on predictive analytics and real-time data.

[0050] Fig. 20 depicts the architecture of the Decision Management Tool - Gen AI Module within the disaster management system. It illustrates how vector data, forecast data, and real-time monitoring data are processed through data storage and query-building tools (Java and Python) to generate AI-driven disaster scenarios and decision-making insights. This architecture supports dynamic query generation for disaster response.

[0051] Fig. 21 depicts the Dissemination Module within the disaster management system. It shows how system-generated alerts, such as bulletins and warnings, are distributed through external network systems for real-time dissemination. This module ensures timely communication of disaster alerts to responders and the public.

[0052] Fig. 22 depicts the architecture of the Dissemination Module within the disaster management system. It shows how alerts are distributed through various channels, including departmental alerts (email, mobile apps, WhatsApp) and public alerts (SMS, TV, print media, social media).

[0053] Fig. 23 depicts the Deployment Module within the disaster management system. It shows the tracking of resources, responders, and field teams using GPS and handheld devices. This module provides real-time visualization of deployment status through a dashboard, enabling efficient resource management during disaster operations.

[0054] Fig. 24 depicts the architecture of the Deployment Module within the disaster management system. It illustrates how vector and real-time data are processed using Java and Python, then transmitted via APIs to provide real-time tracking of vehicles, field teams, and resources. The module includes a visualization dashboard that tracks deployments and generates heatmaps for efficient resource management during disaster operations.

[0055] Fig. 25 depicts the Evacuation & Relief Operations Module within the disaster management system. It shows the tracking and monitoring of relief camps and resources using handheld devices. This module provides real-time visualization of relief operations through a status dashboard, enabling efficient coordination during evacuation and relief efforts.

[0056] Fig. 26 depicts the architecture of the Evacuation & Relief Operations Module within the disaster management system. It illustrates how vector and real-time data are processed using Java and Python, and how data is transmitted via APIs to a visualization dashboard. This module provides real-time monitoring of relief operations, enabling effective management and coordination of evacuation and relief efforts.

[0057] Fig. 27 depicts the Damages & Restoration Module within the disaster management system. It shows the collection of vector data and onsite geotagging using handheld devices to monitor damage and restoration efforts. This module provides real-time visualization through a dashboard, enabling effective tracking of damage assessments and restoration operations.

[0058] Fig. 28 depicts the architecture of the Damages & Restoration Module within the disaster management system. It shows how vector and real-time data are processed using Java and Python, and how the data is transmitted via APIs to a visualization dashboard. This module supports real-time monitoring of damage assessments and restoration operations, providing critical insights through a status dashboard.

[0059] Fig. 29 depicts the Enumeration & Compensation Module within the disaster management system. It shows the use of vector data and handheld devices for onsite geotagging to collect household data and assess eligibility for compensation. The module provides real-time mapping and status monitoring through a dashboard for identifying compensation eligibility and managing enumeration operations.

[0060] Fig. 30 depicts the architecture of the Enumeration & Compensation Module within the disaster management system. It shows how vector and real-time data are processed using Java and Python, and how this data is transmitted via APIs to provide real-time mapping, status monitoring, and compensation eligibility identification through a dashboard.

[0061] Fig. 31 depicts the Analytics, Reports & PPT - AI Module within the disaster management system. It shows how stored data from the entire system is analyzed and used to generate AI-driven reports and presentations. This module provides comprehensive insights through analytics and automatically generates presentations and reports based on disaster-related data.

[0062] Fig. 32 depicts the architecture of the Analytics, Reports & PPT - AI Module in the disaster management system. It shows how data from the system database is processed using Python and AI algorithms to generate analytics, reports, and AI-driven presentations. The module automates report generation based on disaster data for effective analysis and decision-making.

[0063] Fig. 33 depicts the Module Dependency Architecture of the system. It illustrates the interaction and data flow between various modules, such as Vector Data, Forecast Data, Real-Time Monitoring, and others, all interconnected through an API gateway. The diagram highlights the internal and external data exchange within the system, ensuring seamless coordination and real-time disaster management operations.

[0064] FIG. 34 is a block diagram illustrating the details of a digital processing system in which various aspects of the present disclosure are operative by execution of appropriate software instructions.

[0065] FIG. 35 is a flow diagram illustrating the overall operation of the disaster management system, starting from data collection, analysis, decision support, dissemination of alerts, resource deployment, and concluding with post-disaster operations to ensure efficient disaster response and recovery.

[0066] FIG. 36 is a flow diagram illustrating the sub-functional processes within the disaster management system, detailing the steps of data collection, analysis, decision support, alert dissemination, resource tracking, monitoring of relief operations, and post-disaster data feedback for continuous system improvement.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0067] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0068] The use of "including", "comprising" or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the use of terms "first", "second", and "third", and so forth, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0069] Referring to Fig. 1 depicts the overall architecture of the system, showing the Unified Disaster Management Control Module (114) connecting both the client-side and server-side over a network (104) to manage disaster operations effectively. This figure illustrates the communication framework and interaction between various components involved in disaster management, ensuring real-time data handling, decision-making, and response. The computing device (102), which could be a mobile phone, desktop, or any other client-side interface, may be used by field operatives or other stakeholders to interact with the system. This device is connected to a network (104), which serves as the bridge facilitating seamless communication between the client-side and the server-side.

[0070] On the server-side, the server (106) may handle the heavy computational tasks and data storage responsibilities. The server includes a processing unit (110) for managing real-time data processing and executing complex algorithms, and a server memory unit (112), which stores the necessary data and instructions for the disaster management system. This ensures that data received from the client-side computing devices is processed efficiently and decisions can be made based on real-time insights. The system further includes a memory unit (108) on the client-side, which may temporarily store data collected by the computing device (102) before transmitting it over the network (104) to the server (106). This data could include location-based information, sensor readings, or any disaster-related data.

[0071] At the core of this architecture is the Unified Disaster Management Control Module (114), which integrates all the operations and communication between the client-side and server-side. This module may manage data collection, decision-making, and resource deployment across the system, ensuring that all disaster operations are handled in a coordinated and efficient manner. The Unified Disaster Management Control Module (114) acts as the backbone of the system, allowing each element-be it the client-side computing device (102) or the server (106)-to perform its designated tasks while maintaining a cohesive flow of operations.

[0072] Referring to Fig. 2 depicts the functional submodules of the Unified Disaster Management Control Module (114), outlining the key submodules on both the client-side (computing devices (202)) and the server-side (data processing (204)). This figure shows how the various submodules, responsible for different stages and functionalities of disaster management, may be integrated into the system to ensure seamless operations. The computing device (202) on the client-side may interact with multiple functional submodules, allowing field operatives or end-users to gather, transmit, and receive critical data during disaster events. The server (204) on the server-side is tasked with processing the data, executing predictive analytics, and managing large datasets in real-time.

[0073] The Unified Disaster Management Control Module (114) includes several submodules that play distinct roles in managing disaster operations. Vector Data Module (206), this submodule may gather and process geo-tagged vector data from various sources, such as government databases and mobile applications. It provides a detailed spatial representation of assets and infrastructure, essential for decision-making during disasters. Evacuation & Relief Operations Module (208), this submodule is responsible for planning and monitoring evacuation and relief operations. It may track the status of relief camps, evacuation routes, and resource allocation in real time, ensuring that operations are executed efficiently. Deployment Module (210) may oversee the allocation and tracking of emergency responders and resources such as vehicles and personnel. By using GPS tracking and heatmaps, it ensures that resources are deployed to areas most in need during disaster scenarios.

[0074] Real Time Data Monitoring Module (212), this submodule continuously monitors various data points in real-time, such as environmental conditions, resource movements, and incidents. It may gather data from IoT sensors, mobile apps, and social media, providing a live feed to the central system for situational awareness. Post-disaster, Damages and Restoration Module (214) module may be used to assess damage to infrastructure, track restoration efforts, and manage the rebuilding process. It ensures a smooth transition from response to recovery. Enumeration & Compensation Module (216), this submodule handles the enumeration of affected populations and properties. It may use geotagging and mobile applications to identify eligible individuals for compensation, ensuring transparency and accuracy in relief distribution.

[0075] Forecast Data Module (218), this module may gather predictive data from multiple sources such as weather services and government agencies, enabling the system to forecast potential disaster events and allow for proactive planning. Dissemination Module (220) responsible for sending alerts and updates, the Dissemination Module may broadcast critical information to responders and the public via multiple channels, including SMS, social media, and TV broadcasts. Decision Management System - Gen AI Module (222), Leveraging AI and ML, this submodule may provide decision support by analyzing data, generating disaster scenarios, and recommending response strategies. It enhances the speed and accuracy of decision-making during disaster events. Analytics, Reports & Presentation Module - AI Module (224), this submodule may analyze data collected across the system, generate reports, and present insights in a user-friendly format. It could be used to assess the overall disaster response and recovery efforts, offering actionable recommendations.

[0076] Referring to Fig. 2, it depicts the functional submodules of the Unified Disaster Management Control Module (114), outlining the key submodules on both the client-side (computing devices) and server-side (data processing). According to the non-limiting exemplary embodiment of the present invention, the innovation, referred to as DM-360, is an integrated disaster management system designed to function across multiple stages of a disaster-namely pre-disaster, during disaster, and post-disaster-by leveraging a series of interlinked and independent modules. These modules, as illustrated in Fig. 2, can operate either independently or interdependently based on real-time disaster conditions, offering a 360-degree approach to managing various types of disasters, including natural calamities, fire accidents, transportation accidents, and crowd management issues.

[0077] The primary objective of the system is to ensure that each module, such as the Vector Data Module (206) or the Evacuation & Relief Operations Module (208), can function independently during specific disaster stages. However, depending on the real-time situation, these modules may interlink and coordinate their operations to respond effectively. Disasters that the system addresses range from natural events like floods and earthquakes to human-caused incidents, including vehicle accidents and stampedes due to uncontrolled crowds.

[0078] The Unified Disaster Management Control Module (114) integrates several key functional modules that correspond to different stages of disaster management. The Vector Data Module (206) and Forecast Data Module (218) primarily operate during the pre-disaster phase. These modules gather and analyze geographic and forecast data to help prepare for potential disasters by identifying vulnerable areas and predicting the likelihood of events such as floods or storms. The Evacuation & Relief Operations Module (208) plays a role during both the pre-disaster and during-disaster stages, coordinating evacuation efforts and resource allocation to ensure that affected populations are safely relocated to designated relief centers.

[0079] During the disaster itself, the system activates additional modules, such as the Real-Time Data Monitoring Module (212), which continuously monitors the situation through IoT-based sensors, mobile applications, and other real-time data sources. The Decision Management System - Gen AI Module (222) uses predictive algorithms to analyze this real-time data and assist decision-makers in determining the best course of action. The Dissemination Module (220) ensures that real-time alerts and updates are delivered to relevant stakeholders through multiple channels, including SMS, social media, and TV broadcasts. Meanwhile, the Deployment Module (210) manages the deployment of emergency response teams and resources, ensuring they are sent to the areas that need them most.

[0080] In the post-disaster phase, the system transitions to recovery operations, utilizing the Damages and Restoration Module (214) to assess the extent of the damage to infrastructure and support restoration efforts. The Enumeration & Compensation Module (216) works to identify affected individuals and manage the compensation process based on eligibility criteria, ensuring that victims receive the necessary financial support. Lastly, the Analytics, Reports & Presentation Module - AI Module (224) aggregates data from all other modules and generates comprehensive reports and analytics, providing insights that can improve future disaster response strategies.

[0081] The system, as depicted, works in a modular, plug-in manner, meaning it can be adapted to integrate with third-party tools based on the type and scale of the disaster. For instance, in the event of a train accident, the system would directly activate the modules functioning during the disaster stage, bypassing those related to the pre-disaster phase. This modularity ensures that the system is flexible and responsive, tailored to the specific demands of any given situation. Moreover, over time, the system learns from each disaster, refining its data sets based on real-time incident monitoring and functioning. While the system initially operates without the ability to forecast, it evolves over time, learning from real-time data and eventually developing the capability to predict future disasters based on accumulated knowledge.

[0082] In Fig. 2, comprehensively illustrates how the Unified Disaster Management Control Module (114) organizes these functional submodules to ensure seamless disaster management across pre-disaster, during disaster, and post-disaster phases. The system's flexibility, adaptability, and learning capabilities make it a highly effective tool for managing various types of disasters in real-time. The Unified Disaster Management Control Module (114) integrates all these submodules into a cohesive system, facilitating smooth data flow between the computing devices (202) and the server (204). Each submodule may perform specific tasks that contribute to the broader goal of managing disaster operations efficiently across different stages-ranging from real-time monitoring to post-disaster restoration and compensation management.

[0083] Referring to Fig. 3, it depicts the Vector Data Module (206) on the client-side, which collects and integrates geo-tagged data through various interfaces, contributing significantly to disaster analysis and planning. In this module, the Geo-Tagging Interface (302) acts as a central point for collecting location-based information about infrastructure, assets, and geographic boundaries, allowing the system to map critical points relevant to disaster management. This geo-tagged data is gathered through Mobile Data Input (304), wherein users on the ground, including field workers and government agencies, can input real-time location-specific data using mobile applications. This data may include details of affected infrastructure, relief centers, or areas vulnerable to disaster impacts.

[0084] Once the geo-tagged data is captured, it is processed through the GIS Layering Tool (306), which helps visualize this information on multi-layered geographic maps. These layers may represent different aspects such as terrain, infrastructure, population density, and more. The GIS Layering Tool (306) enables the system to integrate multiple datasets into a coherent, easily interpretable format that aids in disaster prediction and planning. The module, by gathering and organizing spatial data from various sources, provides a foundational structure for other modules to work effectively. It ensures that the system is equipped with accurate, up-to-date information on the geographic layout of the region being monitored, which may be essential in anticipating and responding to disasters.

[0085] The Vector Data Module (206) thus plays a critical role in the pre-disaster phase by providing comprehensive geographic data that helps the system forecast potential disaster impacts and plan evacuation or relief operations. Additionally, as the data is continuously updated via the Mobile Data Input (304), the module can adapt to changing conditions, ensuring that the disaster management system has access to the most current information possible. This flexibility ensures that decision-makers are well-informed and can make proactive decisions to mitigate risks and enhance the effectiveness of disaster response strategies.

[0086] Referring to Fig. 4, it depicts the Real-Time Data Monitoring Module (212) on the client-side, which integrates various data sources such as IoT sensors, GPS data, and crowdsourced information for continuous monitoring of disaster conditions. The IoT Sensors Integration (402) plays a crucial role by collecting real-time environmental data like temperature, humidity, water levels, and other relevant metrics from sensors placed in strategic locations. These sensors may provide the system with constant updates about the conditions in areas prone to disaster, allowing for timely interventions and updates. Simultaneously, the GPS Data Input (404) provides real-time location tracking of responders, vehicles, and other essential assets, helping the system visualize the ongoing movement of critical resources. This input may also be used to track the movement of people in affected areas, providing valuable insights into crowd control and evacuation efforts. The GPS data, when combined with real-time environmental data, creates a holistic view of the disaster situation as it unfolds.

[0087] The Crowdsourced Data Processing (406) feature leverages information from individuals on the ground who may report incidents, conditions, or other pertinent data through mobile applications and social media platforms. This enables the system to have eyes and ears across a wide geographical area, supplementing the data gathered from formal sources like sensors and GPS. Such crowdsourced information may provide immediate alerts about emergent situations like sudden floods, fires, or other hazards that may not be detected quickly through automated systems. Additionally, the Drone and CCTV Data Collection (408) adds another layer of real-time monitoring. By using drones and existing CCTV networks, the system can capture live video feeds and images of affected areas. This data is invaluable for assessing the severity of a disaster and for making informed decisions on deploying relief resources. The Real-Time Data Monitoring Module (212) thus serves as a dynamic, constantly updating interface that integrates multiple data streams to provide a comprehensive, real-time view of disaster conditions. This module plays an essential role in the "during disaster" phase, helping to ensure that responses are timely, accurate, and based on the most current information available.

[0088] Referring to Fig. 5, it depicts the Evacuation & Relief Operations Module (208) on the client-side, responsible for tracking and managing the movement of resources and evacuees during disaster relief operations. This module integrates multiple functionalities that may assist in ensuring smooth evacuation processes and efficient resource allocation during critical times. One of the key features of the module is the GPS Tracking for Evacuation (502), which allows for real-time tracking of evacuees, vehicles, and personnel. This feature ensures that those in need of evacuation can be moved safely, and the locations of relief forces and resources can be continuously monitored to avoid delays or mismanagement.

[0089] In addition to tracking, the system also generates Heatmaps of Resource Distribution (504), which visualize the concentration of available resources such as medical supplies, food, and shelter across affected regions. These heatmaps help in identifying areas that may require immediate attention, highlighting zones with insufficient resources so that the required materials can be dispatched promptly. Another critical aspect of this module is Relief Camp Monitoring (506), where the system keeps track of the status and occupancy levels of various relief camps established in response to the disaster. It may monitor aspects such as the number of people present, availability of supplies, and the overall capacity of these camps, ensuring that evacuees are directed to the most appropriate locations based on real-time needs.

[0090] The Real-Time Status Dashboard (508) provides a consolidated view of the entire evacuation and relief effort. It offers decision-makers a visual and continuously updated interface to track the overall progress of evacuations, resource distribution, and camp management. The dashboard may present real-time data in an intuitive format, allowing for quick adjustments in strategy as new information becomes available. Together, these functionalities ensure that the Evacuation & Relief Operations Module (208) provides a robust tool for coordinating efforts during the critical "during disaster" and "post-disaster" phases, optimizing both the evacuation process and resource distribution.

[0091] Referring to Fig. 6, it depicts the Deployment Module (210) on the client-side, focusing on real-time tracking of responders, vehicles, and relief resources using satellite-based communication tools, mobile applications, GPS, and heatmap visualization. The GPS Tracking of Responders and Vehicles (602) is a crucial feature that may enable the system to continuously monitor the locations and movements of emergency personnel, vehicles, and other critical assets in the field. This tracking ensures that responders can be directed to the most affected areas with precision, improving the overall effectiveness of disaster response operations.

[0092] The module also incorporates Heatmap Visualization of Deployed Resources (604), which may display the concentration of deployed personnel and resources across the disaster-affected region. The heatmap provides a visual representation of areas with high or low resource deployment, allowing decision-makers to quickly identify where additional resources are required and where there may be excess personnel or equipment that can be redirected. In addition, the Real-Time Deployment Dashboard (606) serves as the central interface for managing and overseeing the deployment of all resources. This dashboard may present a consolidated, live view of the deployment status of various assets, including responders, vehicles, and equipment, allowing operational leaders to make informed decisions on the fly. The dashboard can provide real-time data on the location, availability, and status of resources, enabling efficient adjustments in deployment strategies as the disaster situation evolves. By integrating these functionalities, the Deployment Module (210) ensures that disaster relief efforts are well-coordinated, allowing for the timely and effective distribution of resources where they are most needed.

[0093] Referring to Fig. 7, it depicts the Damages & Restoration Module (214) on the client-side, which handles the assessment of damages and the monitoring of restoration efforts through geotagging and real-time updates. The module integrates various functionalities aimed at providing accurate and timely information about damage assessments and the progress of restoration activities. The Damage Tagging Interface (702) allows users to geotag damaged infrastructure and areas affected by the disaster. This interface may enable responders and field personnel to mark the locations of damaged assets directly from the field, creating a detailed map of the affected zones.

[0094] The module also includes a Real-Time Restoration Status (704) feature, which tracks ongoing restoration efforts. This functionality ensures that decision-makers have up-to-date information about the progress of repairs and rebuilding activities. The system may monitor key aspects of restoration work, such as the availability of resources, the status of critical infrastructure repairs, and the timelines for project completion. Additionally, the Infrastructure Geotagging Tool (706) provides more precise tagging of various infrastructure components, including roads, bridges, power lines, and other critical facilities. This tool may assist in the detailed mapping of infrastructure that has been damaged and help prioritize the restoration process by identifying the most critical assets that need attention.

[0095] The Mobile App for Field Data Collection (708) further enhances the functionality of this module by allowing field workers to collect data directly from the disaster site. This data may include photos, descriptions, and geotagged locations of damaged structures, which can then be uploaded to the central system in real-time. The mobile app ensures that all relevant information from the field is collected accurately and promptly, supporting a more efficient and coordinated restoration process. Overall, the Damages & Restoration Module (214) plays a vital role in the post-disaster phase, ensuring that damage assessments are thorough and restoration efforts are tracked and managed effectively.

[0096] Referring to Fig. 8, it depicts the Enumeration & Compensation Module (216) on the client-side, which processes affected household data and determines eligibility for compensation through real-time mapping and status monitoring. This module plays a crucial role in the post-disaster phase by ensuring that individuals and households impacted by the disaster are accurately enumerated and appropriately compensated. The Household Data Input (802) feature allows field personnel to collect detailed information about affected households, including the number of family members, the extent of property damage, and any specific needs. This data may be gathered through mobile applications and uploaded in real-time to the system for further processing.

[0097] Additionally, the module incorporates Geotagging for Affected Areas (804), which helps map the specific locations of impacted households and communities. This geotagging functionality may provide a spatial view of the disaster's impact, enabling authorities to identify which areas require the most urgent assistance. The geotagged data allows for more efficient allocation of resources and supports accurate decision-making during the relief process. The Compensation Eligibility Dashboard (806) presents a real-time view of households and individuals who are eligible for compensation based on predefined criteria. This dashboard may display the current status of each case, including whether an application has been approved, is pending, or requires additional verification. By automating the eligibility assessment process, this feature ensures that relief funds are distributed fairly and efficiently. The Status Reports on Relief Efforts (808) provide continuous updates on the progress of compensation and relief operations. This feature allows decision-makers and relief organizations to monitor how compensation is being distributed and track the overall effectiveness of the relief efforts. The Enumeration & Compensation Module (216) thus ensures that affected individuals receive the support they need in a timely and organized manner, making it a key component of the system's post-disaster management strategy.

[0098] Referring to Fig. 9, it depicts the Forecast Data Module (218) on the server-side, which processes weather and environmental forecast data from various sources to support predictive disaster management. This module is essential for enabling the system to anticipate and prepare for potential disasters by analyzing incoming forecast data. The Data Retrieval from Government/Private Sources (902) feature allows the module to pull forecast data from a wide range of sources, including government meteorological services, private weather agencies, and satellite-based systems. This ensures that the system has access to the most comprehensive and up-to-date weather data available.

[0099] The module utilizes Scenario-based Forecast Models (904) to simulate various disaster scenarios based on the retrieved data. These models may predict the likelihood of different disaster events, such as floods, storms, or heatwaves, by analyzing historical trends and real-time environmental conditions. This scenario-based modeling supports decision-makers by offering insights into potential future events, enabling them to take preemptive actions. The Data Integration for Weather Events (906) feature consolidates multiple streams of forecast data, combining real-time data with historical records to offer a more nuanced understanding of weather patterns and environmental risks. By integrating this data into a cohesive format, the system may generate more accurate and reliable predictions, improving the overall preparedness for upcoming disaster events. Additionally, the Visualization of Forecast Data (908) provides an intuitive, graphical representation of forecast information. This feature may display forecast maps, weather patterns, and disaster risk zones, allowing decision-makers to quickly interpret the data and implement necessary measures. The Forecast Data Module (218), as shown in Fig. 9, plays a critical role in supporting proactive disaster management by delivering timely and accurate forecasts, which helps to mitigate the impact of potential disaster scenarios.

[00100] Referring to Fig. 10, it depicts the Decision Management Tool - Gen AI Module (222) on the server-side, which leverages AI and ML technologies to provide scenario-based decision-making and real-time disaster response recommendations. The module serves as a critical component in automating and enhancing the decision-making process during disaster situations. The Scenario Generation Engine (1002) is responsible for creating various disaster scenarios based on real-time data, historical trends, and forecasted conditions. This engine may analyze multiple data inputs to simulate different potential disaster outcomes, helping authorities visualize how a situation might evolve and what actions are necessary to mitigate risks. The Predictive Analytics (1004) feature further strengthens the system's capabilities by using machine learning algorithms to analyze data trends and predict future disaster events. This component processes large datasets, including environmental data, previous disaster records, and real-time monitoring information, to generate accurate predictions. These insights allow decision-makers to take proactive steps, potentially preventing a disaster from escalating or ensuring that resources are allocated more effectively.

[00101] Additionally, the Real-Time Decision Support (1006) function provides actionable recommendations during the disaster. Based on the scenarios generated and predictions made, this feature may offer optimized strategies for resource deployment, evacuation routes, or emergency response measures. The system continuously processes incoming data to refine its recommendations, ensuring that responses remain effective even as the disaster evolves. The AI-Based Alert Generation (1008) allows the module to automatically generate alerts based on its analysis and decision-making processes. These alerts may be sent to relevant stakeholders, including responders and the public, through various communication channels. The alerts can provide warnings about impending disaster conditions or recommend specific actions to minimize harm. The Decision Management Tool - Gen AI Module (222), as depicted in Fig. 10, plays a pivotal role in disaster management by using advanced AI and ML technologies to ensure that decisions are informed, timely, and based on the most accurate data available.

[00102] Referring to Fig. 11, it depicts the Dissemination Module (220) on the server-side, responsible for sending disaster alerts and notifications to the public and responders through multiple communication channels like SMS, social media, and email. This module plays a vital role in ensuring that critical information reaches the appropriate parties promptly, enhancing the effectiveness of disaster response and public safety. The SMS/Email API (1102) is a key feature that allows the system to send out real-time alerts via text messages and email. These alerts may include warnings, updates on the disaster situation, evacuation instructions, and resource deployment orders. By using SMS and email, the system ensures that communication reaches a wide audience, including individuals in affected areas and emergency responders. Additionally, the Social Media Alerts (1104) feature enables the system to broadcast information through social media platforms. This capability is especially valuable for reaching a broader public audience and for disseminating information quickly. Social media platforms may provide a fast and effective way to share updates and alerts, ensuring that individuals have access to real-time information during a disaster. The system may also leverage user engagement on social media to gather crowdsourced data or confirm the status of certain locations.

[00103] The TV and Radio Notifications (1106) component further extends the reach of the alert system by broadcasting warnings and updates through traditional media channels such as television and radio. These mediums are crucial in situations where mobile networks may be down or unavailable, ensuring that even those without internet access receive timely notifications. This functionality allows the module to maintain communication redundancy, ensuring that critical alerts are broadcasted across different platforms to maximize reach and effectiveness. The Dissemination Module (220), as illustrated in Fig. 11, integrates these communication channels to provide a comprehensive alert system that may ensure both responders and the general public are kept informed and can take appropriate actions based on real-time disaster developments. The flexibility and redundancy in communication methods ensure that alerts are delivered even under challenging circumstances.

[00104] Referring to Fig. 12, it depicts the Analytics, Reports & Presentation Module - AI Module (224) on the server-side, which generates AI-driven reports and presentations based on disaster data, supporting post-disaster analysis and recovery planning. This module plays a crucial role in providing decision-makers with comprehensive insights into the disaster's impact and the effectiveness of the response efforts. The AI-Generated Reports and Presentations (1202) feature allows the system to automatically create detailed reports and presentations that summarize key metrics, trends, and outcomes of the disaster response. These reports may include data on resource deployment, evacuation effectiveness, and damage assessments, providing a high-level overview that aids in strategic planning for future events.

[00105] The Data Integration from All Modules (1204) ensures that the system pulls together information from all other modules within the disaster management system. This integration may include real-time monitoring data, forecast information, and resource tracking, allowing the AI module to analyze a wide range of data points. By bringing together data from various sources, the system is able to offer a comprehensive and holistic analysis of the disaster situation. The Real-Time Analytics Dashboard (1206) provides decision-makers with a live, interactive view of disaster metrics and ongoing operations. This dashboard may display visualizations such as graphs, charts, and heatmaps, offering insights into the current state of the disaster and the progress of recovery efforts. The real-time nature of the dashboard allows for continuous monitoring and adjustments, ensuring that recovery plans are implemented effectively and resources are allocated appropriately.

[00106] Additionally, the Post-Disaster Reporting Tools (1208) offer specialized features for generating reports that focus on the aftermath of the disaster. These tools may analyze data related to restoration efforts, compensation distribution, and infrastructure rebuilding, providing a detailed view of the recovery process. The reports generated by this feature help stakeholders evaluate the success of the disaster response and identify areas for improvement in future disaster management efforts. Overall, the Analytics, Reports & Presentation Module - AI Module (224), as depicted in Fig. 12, enables thorough post-disaster analysis by leveraging AI to process data, generate insightful reports, and provide real-time analytics. This module is instrumental in guiding recovery operations and improving the overall effectiveness of disaster management planning.

[00107] Referring to Fig. 13, it depicts the Vector Data Module (1302) in the disaster management system, which collects and integrates geo-tagged vector data from various sources such as state-owned GIS data, departmental assets, and private establishments. This module plays a crucial role in disaster preparedness by compiling essential spatial data that may be utilized for mapping disaster-prone areas and identifying critical resources. The data is processed and stored in GeoJson Files (1306) for further analysis and visualization within the system. The State Owned Data (1304) component refers to the collection of geo-tagged information about infrastructure, public utilities, and other assets that are managed by government agencies. This data may include details about roads, hospitals, power stations, and other critical infrastructure that could be vulnerable during a disaster. Additionally, data from private establishments may also be integrated into the system to provide a comprehensive overview of the region's assets.

[00108] A Mobile App for Geo-Tagging may be utilized by field personnel to tag locations in real-time, allowing for the immediate collection of relevant data from the field. This feature ensures that the system is updated with the most current information about potential disaster areas and resources available for disaster response. The collected data is then stored in the GIS Data Storage, which organizes all geo-tagged data into a structured format, such as GeoJson Files (1306). This format facilitates seamless access to the data for further processing and analysis. The Vector Data Module (1302) interacts with other components of the disaster management system by feeding spatial data into the predictive modeling and resource allocation processes. By providing up-to-date and accurate geographic information, this module enables decision-makers to identify vulnerable areas, allocate resources more efficiently, and improve disaster preparedness overall. This module is an integral part of the system, ensuring that spatial data is readily available for real-time analysis and decision-making during disaster events.

[00109] Referring to Fig. 14, it depicts the architecture of the Vector Data Module (1402) within the disaster management system. It illustrates the flow of State Owned GIS Data (1404), starting from the collection of geo-tagged information through Mobile Apps for Geotagging (1406) and Web Applications for Resource Mapping (1408). These platforms allow field personnel and other users to capture, tag, and map important spatial data in real-time, which is then transmitted to the system for further processing.

[00110] Once the data is collected, it is stored in a centralized Data Storage (1410) repository, where it is organized for efficient retrieval and management. This stored data includes information on critical infrastructure, public utilities, and private establishments, which are key to disaster planning and response. The Query Building (1412) function, implemented using Java, allows users and system algorithms to access specific data sets based on the needs of disaster management operations. Through this functionality, custom queries may be generated to extract relevant spatial data, enabling informed decision-making and analysis.

[00111] The final step in the architecture is the Visualization (1414) process, where the data is graphically represented for disaster analysis and resource mapping. This visualization provides decision-makers with an intuitive interface that may include maps, layered data points, and visual indicators of resource allocation or risk areas. The Vector Data Module (1402) ensures that all geospatial data is managed and visualized in a structured manner, supporting critical disaster response functions such as predictive modeling, resource allocation, and real-time decision-making.

[00112] Referring to Fig. 15, it depicts the Forecast Data Module (1502) within the disaster management system. It shows the integration of various forecast data sources, including governmental and private agencies, which provide critical weather, flood, and cyclone information. This module gathers, processes, and visualizes forecast data to support proactive disaster response and management. The State Owned GIS Data (1504) acts as a key component, enabling the integration of spatial data with forecast models to generate accurate disaster predictions. The function of this module is to collect weather forecasts and environmental data from diverse sources, including governmental agencies such as the Indian Meteorological Department (IMD), Central Water Commission (CWC), Indian Institute of Tropical Meteorology (IITM), and Joint Typhoon Warning Center (JTWC), along with private weather services. These sources may provide crucial data related to weather patterns, rainfall predictions, floods, cyclones, and high tides. The module processes this data to enable early warnings and informed decision-making.

[00113] The Forecast Data Module (1502) includes advanced Predictive Models, which leverage AI and machine learning algorithms to analyze the incoming forecast data and generate accurate predictions for weather-related disasters. These models may run simulations to assess the potential impact of various weather events and create scenarios that guide disaster preparedness and response efforts. The data formats used within this module, such as NetCDF and GRIB, allow for efficient processing of large datasets and integration with predictive analytics tools. This module interacts with the overall decision-making engine of the disaster management system, providing early warnings and predictive insights. By feeding forecast data into the predictive models, the Forecast Data Module (1502) plays a critical role in enabling better planning and timely responses to potential disasters. Its integration with various data sources and the ability to process and visualize forecasts make it a vital component of proactive disaster management.

[00114] Referring to Fig. 16, it depicts the architecture of the Forecast Data Module (1602) within the disaster management system. It illustrates the process of gathering forecast data from both government and private sources via an FTP Server (1606), followed by processing through various technologies such as Python (1608) and JavaScript (1610) to generate Spatial Distribution Maps (1612). This module is responsible for handling, processing, and visualizing forecast data to support disaster prediction and management. The data sources include Raw Data (1614), which consists of weather and environmental forecasts, acquired through agreements with governmental organizations, denoted as GOI-Forecast Data through MOU (1616), and forecast data procured from private players, represented as Forecast Data Procured from Private Players (1618). These sources provide information in specialized formats such as NetCDF/GRIB (1604), which are standard formats for managing large datasets in meteorological and environmental sciences.

[00115] Once the data is gathered from these sources via the FTP Server (1606), it is processed using Python (1608) for data analysis and transformation. The module then employs JavaScript (1610) to render this data visually, creating Spatial Distribution Maps (1612) that help in understanding how disasters such as floods, storms, or cyclones are likely to impact different regions. These maps provide crucial insights for decision-makers, allowing for better resource allocation and more effective disaster management strategies. The Forecast Data Module (1602) thus functions as a central hub for collecting, processing, and visualizing forecast data. By integrating data from multiple sources and formats, and using advanced programming languages to generate real-time maps, this module ensures that disaster management operations are supported by accurate and timely predictions. This architecture enables the system to deliver spatial insights that guide preemptive actions, such as evacuation plans or emergency preparedness measures, contributing to proactive disaster response.

[00116] Referring to Fig. 17, it depicts the Real-Time Data Monitoring Module (1702) within the disaster management system. This module is designed to collect real-time data from a variety of sources, including IoT-based weather stations, GPS tracking devices, drones, and crowdsourced information. It plays a pivotal role in ensuring continuous monitoring of environmental and incident data, enabling more effective disaster response and management. The module functions by gathering data from IoT Devices, such as automatic weather stations, water gauges, GPS devices, and other environmental sensors. These devices provide real-time measurements of critical conditions, including temperature, humidity, water levels, and geographic positioning, which are essential for monitoring disaster situations like floods or storms. The integration of these devices allows for a constant flow of up-to-date information into the disaster management system.

[00117] In addition to data from IoT devices, the module also leverages Crowdsourcing Tools by gathering information from mobile applications and social media platforms. This crowdsourced data may include real-time reports from individuals in affected areas, enabling the system to receive on-the-ground updates about developing situations, such as blocked roads or rising floodwaters. This information enhances the system's ability to respond dynamically to emerging threats. Once the data is collected, it is processed through Data Processing Pipelines, which organize and structure the information, often in JSON format, for integration into the broader disaster management system. These pipelines may filter, validate, and convert the incoming data into usable formats that can be easily accessed by other modules within the system, ensuring the smooth flow of real-time information. The Real-Time Data Monitoring Module (1702) interacts with other components of the disaster management system by providing continuous, real-time data that enhances the accuracy of disaster forecasts and helps monitor ongoing disaster conditions. By collecting and integrating data from multiple sources, this module ensures that decision-makers have access to the most current information, improving situational awareness and facilitating faster, more informed responses during disaster events.

[00118] Referring to Fig. 18, it depicts the architecture of the Real-Time Data Monitoring Module (1802) within the disaster management system. This architecture illustrates how data from various sources, including IoT devices, GPS tracking systems, mobile apps, and crowdsourced information, is collected through APIs, processed, and continuously monitored in real time. The Real-Time Data Monitoring Module (1802) plays a crucial role in disaster response by providing timely, actionable data that helps decision-makers respond effectively to evolving situations. The module integrates data from IoT devices, such as weather sensors, water level gauges, and environmental monitoring stations, which continuously send real-time measurements about critical environmental factors like temperature, humidity, or rainfall. This data is vital for monitoring potential risks and initiating early warning protocols. GPS tracking systems also contribute location-based data, providing real-time updates on the positions of vehicles, emergency personnel, and other critical assets, enabling the system to manage logistics and resource allocation more efficiently.

[00119] The system also gathers information from mobile apps and crowdsourced inputs provided by individuals reporting incidents in disaster-affected areas. Through mobile applications, users can send geotagged photos, reports, and other crucial details, while social media integration allows the system to track trends and reports related to the disaster. All this data is gathered through APIs, which connect the various data sources to the central system. Once collected, the data is processed in real time, ensuring that the most up-to-date information is available for incident monitoring. The system may also perform data validation and filtering processes to ensure that only accurate and relevant information is fed into the decision-making engine. The continuous flow of data into the Real-Time Data Monitoring Module (1802) allows the system to generate live updates on the disaster situation, offering decision-makers and responders critical insights into the current status of the environment and resource deployment. This architecture supports real-time monitoring and enables rapid, informed responses to dynamic disaster scenarios, significantly improving the effectiveness of disaster management efforts.
[00120] Referring to Fig. 19, it depicts the Decision Management System - Gen AI Module (1902) within the disaster management system. This module demonstrates how data from real-time monitoring, forecast data, and vector data are processed by the AI-driven decision management system. The system integrates these diverse Data Sets (1904), including environmental data, infrastructure information, and live updates from IoT devices, to generate accurate predictive models and actionable insights. The module functions as a key decision-making tool, helping disaster management authorities respond effectively to rapidly changing conditions. The primary function of this AI-powered module is to analyze data from multiple sources and generate automated outputs such as disaster alerts, resource allocation recommendations, and scenario-based predictions. AI/ML Algorithms within the system may analyze historical data in combination with real-time inputs to predict how a disaster may evolve. These machine learning models provide a probabilistic assessment of the situation, allowing decision-makers to anticipate potential risks and implement preemptive measures.

[00121] Additionally, the Scenario Query Builder is a custom-built tool within the module that generates queries based on the data received from other modules, such as the Vector Data and Forecast Data Modules. It simulates various disaster scenarios, offering insights into potential outcomes and the effectiveness of different response strategies. These simulations help authorities understand the best course of action depending on the evolving disaster conditions. The system also generates System Generated Alerts (1906), which may automatically notify relevant stakeholders, such as responders and public authorities, of impending risks or changes in the disaster environment. These alerts, driven by real-time data and predictive models, allow for quick and informed responses, minimizing the impact of the disaster. In its operation, the Decision Management System - Gen AI Module (1902) interacts closely with the Vector and Forecast Data Modules to process spatial and temporal data, ultimately providing disaster management authorities with proactive, data-driven tools for making decisions. This module plays a critical role in enhancing the efficiency and effectiveness of disaster response efforts by offering advanced, AI-powered decision-making capabilities.

[00122] Referring to Fig. 20, it depicts the architecture of the Decision Management Tool - Gen AI Module (2002) within the disaster management system. This figure illustrates how vector data, forecast data, and real-time monitoring data are processed through data storage systems and query-building tools, utilizing programming languages such as Java (2014) and Python (2016) to generate AI-driven disaster scenarios and decision-making insights. The architecture is designed to support dynamic query generation, ensuring rapid and accurate responses during disaster situations. The module relies on three primary databases: the Vector Data Database (2004), the Forecast Data Database (2006), and the Real-Time Monitoring Database (2008). These databases store critical information regarding geographic locations, forecasted weather conditions, and live environmental or situational updates, respectively. The system may fetch specific data points using the Fetching Required Fields (2010) mechanism, which allows it to isolate and analyze relevant data based on the evolving disaster scenario.

[00123] The DMS Data Storage (2012) serves as the centralized repository where all processed and collected data is stored for real-time analysis. The data stored here is then subjected to processing via Java (2014) and Python (2016), two powerful programming languages that are essential for managing and interpreting large datasets. These languages allow for the creation of algorithms and models that can identify patterns and predict potential disaster outcomes. The architecture also includes specialized query-building tools. The Common Area Query Builder (2018) and Polygon Query Builder (2020) may generate spatial queries based on geographic regions affected by the disaster. These tools allow the system to create focused queries targeting specific areas, ensuring that disaster response strategies are precise and data-driven.

[00124] The most critical component of the architecture is the Scenario Query Generator (2022), powered by AI and ML algorithms. This generator may run simulations of disaster scenarios based on the available data, helping decision-makers understand potential outcomes and take proactive measures. By leveraging these AI/ML-based insights, the system generates automated alerts and decision support recommendations. In summary, the architecture of the Decision Management Tool - Gen AI Module (2002), as depicted in Fig. 20, provides a robust framework for processing diverse data types, generating dynamic queries, and producing AI-driven disaster management scenarios. This architecture is essential for ensuring that disaster response strategies are well-informed, timely, and adaptable to real-time conditions.

[00125] Referring to Fig. 21, it depicts the Dissemination Module (2102) within the disaster management system. This module illustrates how system-generated alerts, such as bulletins and warnings, are distributed through external network systems for real-time dissemination. Its primary function is to ensure timely communication of disaster alerts to both responders and the public, allowing for swift, coordinated responses during emergencies. The Data Sets (2104) utilized by this module include the information processed and generated from other core components of the disaster management system, such as real-time monitoring, forecast data, and decision-making insights. Based on these inputs, the module triggers System Generated Alerts (2106), which include notifications regarding evolving disaster conditions, evacuation orders, and important updates.

[00126] These alerts are disseminated through External Network Systems (2108), which encompass a variety of communication channels such as SMS, social media platforms, WhatsApp groups, and mobile applications. This multi-channel approach ensures that the alerts reach a broad audience quickly and efficiently. Additionally, the module may integrate with traditional media outlets like television and radio to broadcast these alerts to ensure maximum coverage. One of the critical components of the system is the Failsafe Communication System (SATARKA), which ensures that communication channels remain functional even in cases where infrastructure may be compromised during a disaster. This feature helps guarantee that vital information is transmitted without delay, even under adverse conditions. The Dissemination Module (2102) interacts closely with the Decision Management Tool, receiving inputs from the AI-driven decision-making engine and ensuring that alerts and warnings are promptly disseminated to the relevant parties. This module functions as the communication hub within the disaster management system, playing a vital role in informing and coordinating response efforts, ultimately improving disaster management operations and public safety.

[00127] Referring to Fig. 22, it depicts the architecture of the Dissemination Module (2202) within the disaster management system. This architecture illustrates how system-generated alerts are distributed through various communication channels, ensuring comprehensive and timely dissemination of critical disaster information. The module ensures that both departmental personnel and the general public receive alerts, helping to coordinate responses and inform affected communities. At the core of the architecture is the Server (2204), which processes the data inputs and triggers alert generation based on disaster conditions. The server interacts with other components of the disaster management system, receiving data from real-time monitoring, decision-making tools, and forecast modules. This data is processed and relayed to the API (2206), which facilitates communication between the server and external systems for alert dissemination.

[00128] The API (2206) manages the transmission of alerts to two primary channels: Departmental Alerts (2208) and Public Alerts (2210). Departmental Alerts may be sent via email, mobile apps, or messaging platforms like WhatsApp, directly notifying key personnel and emergency responders about the current disaster situation. This ensures that departments involved in the disaster response are well-coordinated and can take immediate action based on real-time updates. On the other hand, Public Alerts (2210) are broadcast to the broader public through various channels such as SMS, television broadcasts, print media, and social media platforms. These alerts provide the public with critical information, including evacuation orders, safety guidelines, and real-time updates about the disaster. This multi-channel approach ensures that the information reaches as many people as possible, enhancing public awareness and safety.

[00129] An integral feature of the system is the SATARKA Failsafe Communication System (2212), which ensures the continuous functionality of communication channels even during infrastructure failures. In cases where traditional communication systems may be compromised due to disaster damage, SATARKA ensures that critical alerts are still delivered without delay, providing a reliable backup to ensure consistent communication. In summary, the architecture of the Dissemination Module (2202), as depicted in Fig. 22, demonstrates how the system efficiently distributes alerts to both departments and the public through a variety of channels. This architecture is designed to ensure timely and reliable communication, crucial for effective disaster response and management.

[00130] Referring to Fig. 23, it depicts the Deployment Module (2302) within the disaster management system. This module is responsible for tracking resources, responders, and field teams using GPS and handheld devices, ensuring efficient coordination during disaster operations. The Deployment Module (2302) plays a critical role in managing the allocation and movement of essential resources such as personnel, vehicles, and equipment in real-time. The module integrates various Data Sets (2304), which include information about the locations, availability, and status of responders and resources deployed in the field. These datasets are gathered from GPS tracking systems and input from handheld devices used by field teams. The data may include the geographic position of responders, the status of resource deployment, and the specific areas where teams are engaged in disaster response activities.

[00131] The key feature of the module is the Realtime Deployment Tracking Visualization Tool & Status Dashboard (2306), which provides a comprehensive view of the deployment status across the affected area. This tool allows decision-makers and operational leaders to visualize resource distribution in real time, ensuring that critical resources are deployed where they are most needed. The dashboard may display location-based tracking, updates on team movements, and the status of ongoing disaster response efforts, helping to identify gaps and redirect resources efficiently. By offering real-time visibility into the deployment of resources, the Deployment Module (2302) enhances the system's ability to respond swiftly to dynamic disaster conditions. It supports effective resource management, ensuring that field teams are coordinated, and essential supplies and personnel are allocated optimally to manage the disaster response.

[00132] Referring to Fig. 24, it depicts the architecture of the Deployment Module (2402) within the disaster management system. This architecture illustrates how vector data and real-time data are processed using programming languages such as Java (2410) and Python (2412), then transmitted via APIs to provide live tracking of vehicles, field teams, and resources during disaster operations. The module is equipped with a Realtime Deployment Tracking Visualization Tool & Status Dashboard (2414), which helps in visualizing deployments and generating heatmaps for efficient resource management. The architecture draws data from two key sources: the Vector Data Database (2404) and the Real-Time Data Database (2406). The vector data includes information about geographic locations and infrastructure, while real-time data provides live updates on the current positions and status of resources in the field. This combination of data enables the system to offer an accurate, real-time view of the disaster scenario.

[00133] The Deployment Module (2402) relies on APIs that transmit this processed data to the visualization tools, including Vehicle Tracking (2416) and EFS Team Tracking (2418), which monitor the movement and status of emergency vehicles and field responders. GPS tracking devices installed in these vehicles and handheld devices carried by responders may continuously provide updated location data. The module ensures that all resources are accounted for and can be deployed efficiently based on the disaster's evolving needs. The system also includes Heatmap (2420) visualization tools, which offer a visual representation of resource distribution and movement across the affected areas. The heatmaps allow disaster management authorities to quickly assess where resources are most concentrated and identify regions that require additional support. This module plays a crucial role in disaster response by managing the real-time tracking of resources, enabling better decision-making for emergency operations. It works in conjunction with the Monitoring and Decision Tools, ensuring that resources are optimized and effectively deployed to mitigate the disaster's impact. By integrating vector and real-time data with advanced processing tools, the Deployment Module (2402) ensures smooth and responsive management of disaster relief efforts.
[00134] Referring to Fig. 25, it depicts the Evacuation & Relief Operations Module (2502) within the disaster management system. This module is responsible for tracking and monitoring relief camps and resources in real time, utilizing handheld devices and other technologies to ensure the effective coordination of evacuation and relief efforts. The module integrates multiple Data Sets (2504) that contain information regarding the location and status of relief camps, available resources, and personnel involved in the evacuation process. The Resource Network (2506) forms the backbone of this module, connecting all relief camps and resource hubs. Through this network, data about the availability of supplies, the capacity of relief shelters, and the movements of evacuees is continuously updated. Field personnel use Handheld Devices (2508) to input real-time data regarding on-ground conditions, such as the status of evacuation routes or the occupancy levels of relief centers. These devices allow for real-time reporting and ensure that the most up-to-date information is available to coordinators.

[00135] At the core of the module is the Realtime Relief Operations Monitoring Visualization Tool & Status Dashboard (2510), which provides decision-makers with a comprehensive view of ongoing relief efforts. This dashboard offers real-time visualizations of the location of resources, the status of evacuation operations, and the overall situation at relief camps. It enables authorities to monitor the progress of evacuation efforts, identify any bottlenecks or resource shortages, and adjust strategies to ensure the efficient management of relief operations. The Evacuation & Relief Operations Module (2502) plays a crucial role in disaster response by providing real-time insights and visualizations that help coordinators manage resources and personnel more effectively. It enhances the ability to execute evacuation plans smoothly and ensures that relief operations are carried out efficiently, minimizing delays and optimizing the allocation of resources to the areas most in need.

[00136] Referring to Fig. 26, it depicts the architecture of the Evacuation & Relief Operations Module (2602) within the disaster management system. This architecture demonstrates how vector and real-time data are processed using Java (2610) and Python (2612) and transmitted via APIs to a visualization dashboard, enabling real-time monitoring and coordination of evacuation and relief efforts. The module plays a pivotal role in disaster response, ensuring that relief operations are managed effectively, and that resources are deployed where they are needed most. The module integrates data from two key databases: the Vector Data Database (2604), which stores geographic information about infrastructure, relief camps, and vulnerable areas, and the Real-Time Data Database (2606), which provides live updates on the status of ongoing operations. These databases interact with the module's core processing tools, including Java (2610) and Python (2612), to analyze and structure the data for efficient real-time visualization.

[00137] The system transmits this processed data to the Realtime Deployment Tracking Visualization Tool & Status Dashboard (2614), which serves as a critical interface for decision-makers and field coordinators. This dashboard provides real-time visualizations of relief operations, including the status of relief camps, the movement of evacuees, and the availability of essential resources. This information helps authorities track the progress of operations and adjust strategies dynamically as conditions evolve. The module also incorporates a Mobile App for Damage Tagging, which allows field personnel to geotag damaged infrastructure and report real-time updates from the ground. This app provides valuable insights into the current state of infrastructure and the needs of affected areas, ensuring that relief efforts are directed to the areas most in need of attention.

[00138] Additionally, the Dashboards for Relief Monitoring visualize the status and progress of ongoing relief efforts, offering a comprehensive view of all operations in real-time. The data gathered and processed by this module is fed back into the Decision Management Tool, allowing for continuous assessment and the development of adaptive response plans. Overall, the architecture of the Evacuation & Relief Operations Module (2602) ensures effective real-time monitoring of relief efforts, improving coordination and resource allocation during disaster response. By integrating multiple data sources and providing dynamic visualizations, the system enhances the ability to manage evacuation and relief operations efficiently.

[00139] Referring to Fig. 27, it depicts the Damages & Restoration Module (2702) within the disaster management system. This module is responsible for collecting vector data and utilizing onsite geotagging through Handheld Devices (2708) to monitor damage and restoration efforts. It plays a crucial role in tracking and assessing the extent of infrastructure damage, as well as managing the ongoing restoration operations in real time. The Data Sets (2704) managed by this module include information on the current status of damaged areas, infrastructure, and resources involved in the restoration process. The module integrates Vector Data (2706), which provides geographical information related to the damaged sites, helping to prioritize areas requiring immediate attention. The use of Handheld Devices (2708) by field personnel enables accurate geotagging of affected locations, ensuring that the system receives real-time updates from the disaster-impacted areas.

[00140] A key component of this module is the Realtime Damage Tagging & Restoration Operations Monitoring Visualization Tool & Status Dashboard (2710). This dashboard provides real-time visualization of ongoing restoration activities, including damage assessments, repair progress, and resource allocation. It allows decision-makers to monitor the status of restoration operations, ensuring that the most critical areas are addressed first and that resources are efficiently deployed. By offering continuous visibility into both the damage assessment and restoration processes, the Damages & Restoration Module (2702) helps ensure that response efforts are timely, accurate, and well-coordinated. The real-time data and visualizations provided by the dashboard assist disaster management authorities in making informed decisions, leading to more effective tracking of damage assessments and restoration operations throughout the recovery phase.

[00141] Referring to Fig. 28, it depicts the architecture of the Damages & Restoration Module (2802) within the disaster management system. This architecture illustrates how vector and real-time data are processed using Java (2810) and Python (2812), and how the data is transmitted via APIs to a Realtime Damage Tagging & Restoration Operations Monitoring Visualization Tool & Status Dashboard (2814). The module is designed to provide real-time monitoring and critical insights into damage assessments and restoration operations, supporting efficient disaster recovery efforts. The architecture leverages two primary data sources: the Vector Data Database (2804), which stores geographic information about infrastructure and affected areas, and the Real-Time Data Database (2806), which continuously updates the system with live data from the field. These datasets are essential for accurately mapping the extent of damage and coordinating ongoing restoration activities. The system processes this data using Java (2810) and Python (2812) to analyze and generate meaningful insights, ensuring that the data is structured and readily available for real-time use.

[00142] Through this data processing, the system transmits the analyzed information to the Realtime Damage Tagging & Restoration Operations Monitoring Visualization Tool & Status Dashboard (2814). This dashboard offers decision-makers a comprehensive view of damage assessments, highlighting areas that require immediate attention and tracking the progress of restoration efforts. It may provide visualizations such as heatmaps, status indicators, and location-based tags, ensuring that authorities can monitor the ongoing recovery process efficiently. The integration of vector and real-time data in this architecture supports continuous updates and real-time tracking of the restoration process. Field personnel may use iOS (2808) handheld devices to geotag damaged areas, feeding this information into the system, which further enhances situational awareness. By offering timely and accurate insights, the Damages & Restoration Module (2802) plays a critical role in disaster recovery, helping ensure that restoration efforts are prioritized and executed effectively.

[00143] Referring to Fig. 29, it depicts the Enumeration & Compensation Module (2902) within the disaster management system. This module is designed to collect household data through onsite geotagging using Handheld Devices (2906) and leverage Vector Data (2904) to assess eligibility for compensation. It plays a critical role in identifying those affected by a disaster and ensuring that eligible individuals receive the necessary support. The system utilizes vector data to map the locations of affected households and public infrastructure, allowing for precise geotagging and data collection. Field personnel, equipped with handheld devices, may capture real-time data from the field, including household information, property damage assessments, and other critical details. This data is geotagged and integrated into the system to ensure accurate, location-based tracking of affected individuals and areas.

[00144] At the core of this module is the Realtime Enumeration Mapping, Status Dashboard & Compensation Eligibility Identification (2908), which provides a comprehensive visualization of the enumeration process. The dashboard enables real-time tracking of the number of households affected, their locations, and their status in terms of compensation eligibility. This feature ensures that disaster management authorities can monitor the progress of data collection, verify eligibility for compensation, and ensure that relief funds or resources are distributed appropriately. By combining vector data with real-time geotagging and mapping, the Enumeration & Compensation Module (2902) helps to streamline the process of identifying and supporting those impacted by a disaster. This real-time data management capability enhances the efficiency of the compensation and enumeration operations, ensuring that those in need receive timely and accurate support.

[00145] Referring to Fig. 30, it depicts the architecture of the Enumeration & Compensation Module (3002) within the disaster management system. This architecture demonstrates how Vector Data (3004) and Real-Time Data (3006) are processed using Java (3010) and Python (3012) to generate meaningful outputs for real-time mapping, status monitoring, and compensation eligibility identification. The data is transmitted via APIs to the Realtime Enumeration Mapping, Status Dashboard & Compensation Eligibility Identification (3014), which facilitates accurate and timely decision-making during post-disaster relief efforts. The Vector Data Database (3004) stores geographical information related to affected households and critical infrastructure, while the Real-Time Data Database (3006) continuously updates the system with real-time information gathered from the field using iOS (3008) handheld devices. These handheld devices are used by field personnel to capture and geotag household information, including damage assessments and eligibility details for compensation.

[00146] The data is processed through Java (3010) and Python (3012) to enable the system to handle large datasets efficiently, ensuring seamless integration of vector and real-time data. This processing is essential for creating accurate and real-time visualizations that are displayed on the Realtime Enumeration Mapping, Status Dashboard & Compensation Eligibility Identification (3014). The dashboard allows decision-makers to visualize the status of affected households, identify those eligible for compensation, and monitor the progress of enumeration operations. This architecture ensures that data flows smoothly from collection to analysis and visualization, providing disaster management authorities with the tools they need to manage the enumeration and compensation process effectively. By combining advanced data processing with real-time monitoring, the Enumeration & Compensation Module (3002) supports efficient post-disaster relief operations, ensuring that affected individuals receive timely support based on accurate data.

[00147] Referring to Fig. 31, it depicts the Analytics, Reports & PPT - AI Module within the disaster management system. This module is responsible for analyzing the stored data from the entire system and using AI to generate comprehensive reports and presentations. By leveraging the available Data Sets (3102), which include real-time, historical, and vector data, the module provides detailed insights and assessments that are critical for understanding the disaster's impact and the efficiency of response operations. The Analytics (3104) component processes the collected data, utilizing advanced algorithms and AI-driven tools to generate meaningful insights. These analytics help disaster management authorities assess the effectiveness of relief efforts, predict future disaster risks, and identify areas for improvement in disaster management strategies. The system may run complex data analysis, including trends, patterns, and forecasts, providing a robust understanding of the situation at hand.

[00148] The module also supports the automatic generation of AI-Generated PPTs and Reports (3106). Based on the processed data, the system may automatically compile presentations and detailed reports that include key metrics, visualizations, and summaries of disaster operations. These reports and presentations may be used by disaster response teams, government agencies, and other stakeholders to review performance, plan future actions, and coordinate recovery efforts. Overall, the Analytics, Reports & PPT - AI Module plays a crucial role in post-disaster analysis and reporting. By providing data-driven insights and generating automated reports, it ensures that disaster management is informed by comprehensive analytics, enabling effective decision-making and strategic planning.

[00149] Referring to Fig. 32, it depicts the architecture of the Analytics, Reports & PPT - AI Module (3202) in the disaster management system. This architecture shows how data from the system Database (3204), (DM360, i.e. disaster management in 360 degrees) is processed using Python (3206) and advanced AI (3208) algorithms to generate comprehensive analytics, reports, and AI-driven presentations. The module is designed to automate the report generation process, providing effective tools for analysis and decision-making based on disaster-related data. The system Database (3204) serves as the central repository where all collected data from various modules, including real-time monitoring, vector data, and forecast information, is stored. The module utilizes Python (3206) for processing large datasets and running complex data analysis routines, ensuring that the information is structured and ready for interpretation. Python is used to facilitate data extraction, processing, and preparation for analytics and report generation.

[00150] AI (3208) algorithms are applied to the processed data, enabling the module to conduct predictive analytics, identify patterns, and extract meaningful insights from disaster data. These algorithms are essential for automating the interpretation of large volumes of data and generating accurate and actionable insights in real time. The Analytics (3210) component within the module is responsible for visualizing and analyzing key metrics, including disaster impact assessments, resource allocations, and response times. This feature provides decision-makers with the tools needed to evaluate the effectiveness of disaster response and recovery efforts. Additionally, the module is capable of automatically generating AI-Generated PPTs and Reports (3212), which include detailed summaries of disaster operations, visualizations, and recommendations for future actions. These reports and presentations are automatically compiled based on the analytics generated, saving time and ensuring that stakeholders have access to clear, data-driven insights for strategic planning and decision-making. In summary, the Analytics, Reports & PPT - AI Module (3202) provides automated analysis and reporting capabilities, allowing disaster management teams to focus on action-oriented decisions supported by real-time, AI-driven insights. This architecture ensures that data is efficiently processed and presented in a meaningful way, enhancing the overall effectiveness of disaster response and recovery efforts.

[00151] Referring to Fig. 33, it depicts the Module Dependency Architecture (3300) of the system. This architecture illustrates the interaction and data flow between various modules, such as Vector Data, Forecast Data, Real-Time Monitoring, and other critical components within the disaster management system. The diagram highlights how these modules are interconnected and communicate with one another through an API Gateway, enabling seamless integration and real-time data exchange. Each module within the system plays a specific role, with the Vector Data Module providing geospatial data on affected regions, the Forecast Data Module supplying weather predictions and environmental risk assessments, and the Real-Time Monitoring Module continuously tracking live conditions through IoT devices and crowdsourced data. These modules work collaboratively by sharing data across the system, allowing decision-makers to maintain a comprehensive view of the disaster scenario.

[00152] The API Gateway acts as the central hub for this data exchange, managing the internal and external communication between modules. It ensures that data flows smoothly from one module to another, supporting real-time processing and the generation of actionable insights. For instance, data from the Real-Time Monitoring Module may be utilized by the Decision Management Tool - Gen AI Module to simulate disaster scenarios and recommend responses. Similarly, the Dissemination Module relies on data from multiple sources to issue alerts and notifications. The Module Dependency Architecture (3300) is designed to provide an efficient and coordinated response by ensuring that all system components are interconnected and capable of sharing data dynamically. This level of integration supports effective disaster management operations, from initial risk assessments to real-time response and post-disaster recovery. The architecture ensures that all modules operate in unison, delivering timely information to stakeholders and optimizing resource allocation during disaster events.

[00153] Referring to FIG. 34 is a block diagram 3400 illustrating the details of a digital processing system 3400 in which various aspects of the present disclosure are operative by execution of appropriate software instructions. The Digital processing system 3400 may correspond to the computing device (or any other system in which the various features disclosed above can be implemented). Digital processing system 3400 may contain one or more processors such as a central processing unit (CPU) 3410, random access memory (RAM) 3420, secondary memory 3430, graphics controller 3460, display unit 3470, network interface 3480, and input interface 3490. All the components except display unit 3470 may communicate with each other over communication path 3450, which may contain several buses as is well known in the relevant arts. The components of Figure 34 are described below in further detail. CPU 3410 may execute instructions stored in RAM 3420 to provide several features of the present disclosure. CPU 3410 may contain multiple processing units, with each processing unit potentially being designed for a specific task. Alternatively, CPU 3410 may contain only a single general-purpose processing unit.

[00154] RAM 3420 may receive instructions from secondary memory 3430 using communication path 3450. RAM 3420 is shown currently containing software instructions, such as those used in threads and stacks, constituting shared environment 3425 and/or user programs 3426. Shared environment 3425 includes operating systems, device drivers, virtual machines, etc., which provide a (common) run time environment for execution of user programs 3426. Graphics controller 3460 generates display signals (e.g., in RGB format) to display unit 3470 based on data/instructions received from CPU 3410. Display unit 3470 contains a display screen to display the images defined by the display signals. Input interface 3490 may correspond to a keyboard and a pointing device (e.g., touchpad, mouse) and may be used to provide inputs. Network interface 3480 provides connectivity to a network (e.g., using Internet Protocol), and may be used to communicate with other systems (such as those shown in Figure 1) connected to the network.

[00155] Secondary memory 3430 may contain hard drive 3435, flash memory 3436, and removable storage drive 3437. Secondary memory 3430 may store the data software instructions (e.g., for performing the actions noted above with respect to the Figures), which enable digital processing system 3400 to provide several features in accordance with the present disclosure. Some or all of the data and instructions may be provided on removable storage unit 3440, and the data and instructions may be read and provided by removable storage drive 3437 to CPU 3410. Floppy drive, magnetic tape drive, CD-ROM drive, DVD Drive, Flash memory, removable memory chip (PCMCIA Card, EEPROM) are examples of such removable storage drive 3437. Removable storage unit 3440 may be implemented using medium and storage format compatible with removable storage drive 3437 such that removable storage drive 3437 can read the data and instructions.

[00156] Thus, removable storage unit 3440 includes a computer readable (storage) medium having stored therein computer software and/or data. However, the computer (or machine, in general) readable medium can be in other forms (e.g., non-removable, random access, etc.). In this document, the term "computer program product" is used to generally refer to removable storage unit 3440 or hard disk installed in hard drive 3435. These computer program products are means for providing software to digital processing system 3400. CPU 3410 may retrieve the software instructions and execute the instructions to provide various features of the present disclosure described above.

[00157] The term "storage media/medium" as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, or solid-state drives, such as storage memory 3430. Volatile media includes dynamic memory, such as RAM 3420. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge. Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus (communication path) 3450. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

[00158] Referring to FIG. 35, it is a flow diagram illustrating the overall operation of the disaster management system, beginning with the step of Collecting geo-tagged data (3502) from the Vector Data Module, which includes weather forecasts from the Forecast Data Module and real-time environmental data from the Real-Time Data Monitoring Module, sourced from IoT devices and crowdsourced inputs. This step ensures that all critical data is gathered in real time for analysis. The collected data is then Analyzed (3504) using AI/ML algorithms within the Decision Management Tool, where predictive models are run based on both real-time and historical data. This analysis generates disaster scenarios that help forecast potential risks and inform future actions. Following the analysis, the system moves to Supporting decision-making (3506) by generating alerts and recommendations. These include strategies for resource deployment, risk mitigation, and evacuation planning, which are displayed on dashboards for decision-makers, enabling informed decisions in real time.

[00159] Next, Disseminating alerts (3508) automatically takes place via the Dissemination Module. Real-time alerts are sent out through various communication channels such as SMS, social media, and mobile apps to inform both responders and the general public of immediate risks and necessary actions. The system also handles Tracking resource deployment (3510) using the Resource Tracking Module. This step visualizes the real-time location and status of emergency responders and equipment through heatmaps, enabling effective resource distribution based on evolving disaster conditions. Finally, Monitoring post-disaster operations (3512) involves assessing the effectiveness of the relief efforts and tracking damages through the Relief Operations Module. This data is fed back into the system, allowing for continuous improvement of the disaster management process and ensuring that lessons learned are incorporated into future operations.

[00160] Referring to FIG. 36, it is a flow diagram illustrating the sub-functional processes within the disaster management system, detailing the steps involved in ensuring efficient disaster response and management. The system begins with Initiating geo-tagging (3602) of infrastructure and resources through the Vector Data Module, allowing the system to map critical assets and locations. This data is complemented by Receiving weather forecasts (3604) from governmental and private data sources using the Forecast Data Module, which provides vital predictive weather information. Simultaneously, the system engages in Capturing real-time environmental data (3606) through IoT devices and crowdsourced inputs with the Real-Time Data Monitoring Module, ensuring constant updates on evolving environmental conditions. Once all the relevant data is collected, the system proceeds to Running AI/ML-based predictive models (3608) on both real-time and historical data within the Decision Management Tool, which analyzes potential disaster impacts.

[00161] Following this analysis, the system focuses on Generating disaster scenarios (3610) based on the data input from various modules, providing a clearer understanding of potential outcomes. These scenarios are then used to Present recommendations (3612) for resource deployment, evacuation strategies, and risk mitigation, which are displayed on dashboards for decision-makers. In conjunction with the generated scenarios, the system provides Actionable insights (3614) through predictive models to improve planning and decision-making during disasters, enabling authorities to take preemptive measures. Once critical insights are generated, the system Activates the Dissemination Module (3616), sending out automated alerts via various communication channels such as SMS and social media, ensuring real-time dissemination of vital information to both responders and the public. This process ensures a structured, data-driven response to disaster events, with continuous data analysis and feedback loops facilitating the system's efficiency and improvement over time.

[00162] Reference throughout this specification to "one embodiment", "an embodiment", or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases "in one embodiment", "in an embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[00163] Although the present disclosure has been described in terms of certain preferred embodiments and illustrations thereof, other embodiments and modifications to preferred embodiments may be possible that are within the principles and spirit of the invention. The above descriptions and figures are therefore to be regarded as illustrative and not restrictive.

[00164] Thus the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description.







, Claims:We Claim:
1. A system for integrated disaster management operations comprising: a Unified Disaster Management Control Module stored in the memory units of both the computing devices on the client side and the server on the server side, operably connected over a network, wherein the Unified Disaster Management Control Module manages a plurality of interlinked and independent functional modules located on both the computing devices and the server, the functional modules being configured to operate across three stages of disaster management: pre-disaster, during-disaster, and post-disaster stages; the system comprising:

at least one Vector Data Module located on the client side and comprising a geo-tagging interface and GIS layering tool, the geo-tagging interface configured to receive geo-tagged data from field devices and the GIS layering tool configured to process and layer the collected geo-tagged data for spatial visualization during the pre-disaster stage;

at least one Forecast Data Module located on the server side and comprising a data retrieval engine and scenario-based forecast models, the data retrieval engine configured to obtain weather forecast data from governmental and private sources, and the forecast models configured to generate risk predictions based on weather patterns and historical data for disaster preparedness during the pre-disaster stage;

a Real-Time Data Monitoring Module located on the client side and comprising IoT sensors, GPS trackers, and crowdsourced data processing tools, the IoT sensors and GPS trackers configured to capture real-time environmental data, and the crowdsourced data processing tools configured to aggregate information from mobile devices and social media during the disaster;

a Decision Management Tool - Gen AI Module located on the server side and comprising AI/ML-based processing engines, the AI/ML processing engines configured to analyze real-time and historical data to generate predictive disaster scenarios and recommend disaster response strategies during the disaster;

a Dissemination Module located on the server side and comprising communication APIs and automated alerting systems, the communication APIs configured to connect to SMS gateways, social media platforms, and email services, and the alerting systems configured to send automated alerts and notifications during the disaster to responders and the public;

an Evacuation & Relief Operations Module located on both the client side and server side, comprising evacuation route planning tools and relief resource management interfaces, the evacuation route planning tools configured to generate optimal evacuation paths, and the resource management interfaces configured to allocate and monitor relief resources during both the pre-disaster and during-disaster stages;

a Deployment Module located on the client side and comprising GPS-based resource tracking tools and a real-time status visualization dashboard, the GPS-based tracking tools configured to monitor the location of resources, responders, and vehicles, and the dashboard configured to display real-time resource distribution via heatmaps during the disaster stage;

a Damages and Restoration Module located on the client side and comprising damage assessment tools and restoration tracking tools, the damage assessment tools configured to geotag affected infrastructure, and the restoration tracking tools configured to monitor the progress of restoration efforts during the post-disaster stage; and

an Enumeration & Compensation Module located on the client side and comprising geotagging interfaces and compensation calculation tools, the geotagging interfaces configured to collect household data from disaster-affected areas, and the compensation calculation tools configured to determine compensation eligibility based on the geotagged data during the post-disaster stage, wherein the Unified Disaster Management Control Module coordinates the operation of the functional modules independently or in an interlinked manner based on real-time disaster conditions, thereby facilitating seamless disaster management across the pre-disaster, during-disaster, and post-disaster stages, and whereby the system is adaptable to integrate with third-party tools depending on the type of disaster, thereby improving response times and optimizing resource allocation.

2. The system of claim 1, wherein the Vector Data Module collects geo-tagged data via a mobile application, allowing field personnel to geotag disaster-prone areas and assets in real-time, thereby dynamically updating the system with geo-tagged data during pre-disaster planning.

3. The system of claim 1, wherein the Forecast Data Module processes weather data through a cloud-based system, automatically updating forecast models based on incoming weather information from third-party meteorological services, thereby continuously refining risk predictions during the pre-disaster stage.

4. The system of claim 1, wherein the Real-Time Data Monitoring Module captures visual data from affected areas by integrating drone and CCTV inputs, thereby enhancing situational awareness during disaster monitoring.

5. The system of claim 1, wherein the Decision Management Tool - Gen AI Module generates disaster response scenarios using a dynamic scenario query builder, based on various disaster types including natural disasters, accidents, and crowd management failures, thereby improving the accuracy of predictive models.

6. The system of claim 1, wherein the Dissemination Module ensures continuous alert transmission through a failsafe communication mechanism, maintaining real-time communication even in the event of network or infrastructure failure during disaster situations.

7. The system of claim 1, wherein the Evacuation & Relief Operations Module adjusts evacuation routes in real-time based on changing disaster conditions, traffic data, and resource availability, thereby improving the safety and efficiency of evacuation efforts.

8. The system of claim 1, wherein the Deployment Module prioritizes and deploys emergency responders and relief resources to high-risk areas identified by real-time data, using a resource allocation engine, thereby optimizing resource distribution during disaster response.

9. The system of claim 1, wherein the Damages and Restoration Module enables field personnel to assess and geotag damaged infrastructure in real-time via a mobile field application, thereby ensuring continuous updates on the progress of restoration during the post-disaster stage.

10. The system of claim 1, wherein the Enumeration & Compensation Module determines compensation eligibility using an AI-based calculation tool, applying predefined criteria based on the severity of damage, household data, and geotagged inputs, thereby improving the efficiency of compensation distribution.

11. The system of claim 1, wherein the Unified Disaster Management Control Module integrates with third-party tools via APIs, enabling the system to adapt to varying disaster types and external data sources, thereby enhancing scalability and adaptability for diverse disaster management scenarios.

12. The system of claim 1, wherein the Real-Time Data Monitoring Module detects environmental hazards such as toxic gases, floods, or fires through an integrated sensor network, thereby enabling early hazard detection and disaster response.

13. The system of claim 1, wherein the Analytics, Reports & Presentation Module automatically generates AI-driven reports and presentations in real-time, based on data gathered from all functional modules, thereby supporting post-disaster analysis and recovery planning.

14. The system of claim 1, wherein the functional modules operate independently or interlink dynamically based on the type of disaster, such that during certain disaster events, only specific modules are activated, while in other scenarios, multiple modules interconnect to provide a comprehensive response, thereby enabling the system to adapt to natural disasters, accidents, or crowd management failures by activating relevant modules in real time.

15. The system of claim 1, wherein the pre-disaster modules, including the Vector Data Module and Forecast Data Module, are configured to adapt based on historical disaster patterns and region-specific risks, thereby enhancing the system's ability to provide customized planning and preparedness strategies tailored to specific geographic regions.

16. The system of claim 1, wherein the Decision Management Tool - Gen AI Module refines its predictive models by continuously learning from real-time disaster incidents and historical data, thereby improving the accuracy of disaster forecasts and response recommendations over time.

17. The system of claim 1, wherein the Unified Disaster Management Control Module dynamically integrates third-party tools in real-time based on specific disaster conditions, thereby allowing the system to augment its capabilities and respond more effectively to unforeseen disaster scenarios.

18. The system of claim 1, wherein the Deployment Module coordinates both local and remote resources, including personnel and equipment, based on proximity and real-time disaster conditions, thereby ensuring optimal resource allocation and faster response times.

19. The system of claim 1, wherein post-disaster data collected from the Damages and Restoration Module and the Enumeration & Compensation Module is fed back into the Unified Disaster Management Control Module, thereby enabling continuous improvement of disaster management strategies and response protocols.

20. A method for integrated disaster management operations comprising:

collecting geo-tagged data using a geo-tagging interface within a Vector Data Module;
receiving weather forecasts from governmental and private data sources using a data retrieval engine in a Forecast Data Module;
capturing real-time environmental data via IoT sensors, GPS trackers, and crowdsourced inputs from mobile applications within a Real-Time Data Monitoring Module;
analyzing the collected data through AI/ML-based processing engines within a Decision Management Tool, running predictive models and generating disaster scenarios based on both real-time and historical data;
supporting decision-making by generating alerts and recommendations for resource deployment, risk mitigation, and evacuation plans, and displaying these insights on graphical user interface dashboards for decision-makers;
disseminating alerts automatically through communication APIs and alerting systems within a Dissemination Module, sending real-time alerts through SMS gateways, social media platforms, and other communication channels to responders and the public;
tracking resource deployment using GPS-based tracking tools and a status visualization dashboard within a Resource Tracking Module, visualizing real-time tracking of emergency responders and equipment with heatmaps for resource distribution; and
monitoring post-disaster operations by assessing damages using a mobile field application and tracking restoration efforts through a status dashboard within a Relief Operations Module, wherein post-disaster data is fed back into the system through the Unified Disaster Management Control Module for continuous improvement of disaster response strategies.

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