AI BASED SYSTEM AND METHOD FOR FLOOD RISK FORECASTING FOR DISASTER RESILIENCE

Abstract

Embodiments of the present disclosure relate to an AI based system (102) and method (400) for flood risk forecasting for disaster resilience. The system (102) includes a memory (204) with processor-executable instructions, which on execution, causes a processor (202) to receive a plurality of data types including historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data. The numerical data is processed using a convolutional neural network (CNN) to generate flood prediction probabilities. Image-based data is processed using a deep learning model based on semantic segmentation to detect and map flood locations. The flood risk predictions are continuously updated based on real-time forecasting data to provide adaptive, real-time insights. An AI-driven feedback unit (214) is generated to continuously assess flood risks and enhance adaptive resilience. The strategic evacuation planning and adaptive resource allocation are supported based on the real-time flood predictions.

Specification

Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of flood risk forecasting. More particularly, the present disclosure relates to an AI based system and method for flood risk forecasting for disaster resilience.

BACKGROUND
[0002] Flood risk management involves the use of various techniques and tools to predict, assess, and mitigate the impact of floods. This field encompasses the study of hydrological and meteorological data, the application of machine learning and deep learning models, and the integration of geographical information systems (GIS) and satellite imagery. The primary goal is to provide accurate and timely flood predictions, enabling effective disaster response and resource allocation. Applications include real-time flood forecasting, risk assessment, and the development of strategic evacuation and resource deployment plans.
[0003] Accurate flood prediction is crucial for minimizing the impact of floods on communities and infrastructure. Effective flood risk management systems aim to provide timely warnings, allowing for the implementation of evacuation plans and the allocation of resources to mitigate damage. These systems integrate various data sources, including historical flood data, satellite imagery, and real-time hydrological measurements, to generate comprehensive flood risk assessments. The use of machine learning and deep learning models enhances the predictive capabilities of these systems, enabling more precise and reliable forecasts. Additionally, continuous monitoring and real-time data processing are essential for adapting to changing environmental conditions and improving the overall resilience of flood-prone areas.
[0004] Flood risk management systems face several challenges, including the need for accurate and timely predictions, the integration of diverse data sources, and the ability to adapt to changing environmental conditions. Traditional machine learning models, while effective in time-series prediction and hydrological modelling, often lack real-time adaptability and rely heavily on well-prepared datasets. Deep learning models, although capable of spatial and temporal analysis, require extensive computational resources and large datasets for training. Fuzzy logic systems offer adaptability in handling uncertainties within the data but may fall short in predictive robustness when dependent on single-source data. These limitations highlight the need for hybrid models that leverage the strengths of multiple approaches to provide more accurate and reliable flood predictions.
[0005] Document US20040133530A1 describes a method for generating normalized inundation maps for flood prediction. However, this method lacks real-time prediction capabilities and does not utilize deep learning models, limiting its effectiveness in dynamic flood situations.
[0006] Document US7136756B1 describes a method for runoff determination to predict flood risks. However, this method is limited to runoff data without comprehensive flood modelling and does not employ deep learning techniques, reducing its predictive accuracy and adaptability.
[0007] Document US20070143019A1 describes a system for location-dependent flood risk recognition. However, this system does not utilize advanced deep learning algorithms for prediction, resulting in less accurate and reliable flood forecasts.
[0008] Document US20110145035A1 describes a method for activity modelling to predict flood risks. However, this method lacks semantic mapping for precise flood detection and does not incorporate deep learning models, limiting its effectiveness in providing accurate flood predictions.
[0009] Document US20190316309A1 describes a method for flood monitoring and management. However, this method lacks advanced predictive models and does not utilize deep learning techniques, limiting its effectiveness in providing accurate and reliable flood forecasts.
[0010] There is thus a need for a flood prediction system that integrates multiple data sources, including historical, satellite, and hydrological data, into a unified model. This system should employ advanced machine learning and deep learning techniques to provide accurate and timely flood predictions. Additionally, the system should incorporate real-time data processing and continuous updates to adapt to changing environmental conditions, ensuring effective disaster response and resource allocation. By addressing these shortcomings, the proposed system aims to enhance flood risk management and improve resilience in flood-prone areas.

OBJECTS OF THE PRESENT DISCLOSURE
[0011] It is a primary object of the present disclosure to provide an AI based system and method for flood risk forecasting for disaster resilience.
[0012] It is another object of the present disclosure to develop a flood risk forecasting system that utilizes advanced machine learning and deep learning techniques to achieve high prediction probabilities, ranging between 0.89 and 0.95, thereby improving the accuracy of flood predictions.
[0013] It is yet another object of the present disclosure to integrate various data types, including historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data, into a unified model for comprehensive flood risk assessment.
[0014] It is another object of the present disclosure to implement a system that continuously updates flood risk predictions based on real-time forecasting data, providing adaptive, real-time insights for dynamic flood situations.
[0015] It is another object of the present disclosure to generate an AI-driven feedback system that continuously assesses flood risks and enhances adaptive resilience, facilitating timely interventions and improved preparedness for future flood events.
[0016] It is another object of the present disclosure to utilize historical flood maps to identify past flood patterns and trends, Flood Risk AI enhances its predictive capabilities, providing more accurate and reliable flood forecasts.

SUMMARY
[0017] The present disclosure relates to the field of flood risk forecasting. More particularly, the present disclosure relates to an AI based system and method for flood risk forecasting for disaster resilience.
[0018] In an aspect of the present disclosure, an AI based system for flood risk forecasting for disaster resilience is disclosed. The system includes a memory with processor-executable instructions, which on execution, causes a processor to receive a plurality of data types including historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data. The numerical data is processed using a convolutional neural network (CNN) to generate flood prediction probabilities. The prediction probabilities range between 0.89 and 0.95. Image-based data is processed using a deep learning model based on semantic segmentation to detect and map flood locations. The location prediction achieves an Intersection over Union (IoU) of 0.91. The flood risk predictions are continuously updated based on real-time forecasting data to provide adaptive, real-time insights. An AI-driven feedback unit is generated to continuously assess flood risks and enhance adaptive resilience. The strategic evacuation planning and adaptive resource allocation are supported based on the real-time flood predictions. The output data including flood risk predictions, flood detection maps, and resource allocation strategies are provided for effective disaster management.
[0019] In another aspect of the present disclosure, an AI based method for flood risk forecasting for disaster resilience is disclosed. The method begins with receiving a plurality of data types including historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data. The numerical data is processed using a convolutional neural network (CNN) to generate flood prediction probabilities. Image-based data is processed using a deep learning model based on semantic segmentation to detect and map flood locations. The flood risk predictions are continuously updated based on real-time forecasting data to provide adaptive, real-time insights. An AI-driven feedback unit is generated to continuously assess flood risks and enhance adaptive resilience. The method further includes supporting strategic evacuation planning and adaptive resource allocation based on the real-time flood predictions and providing output data including flood risk predictions, flood detection maps, and resource allocation strategies for effective disaster management.

BRIEF DESCRIPTION OF DRAWINGS
[0020] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in, and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure, and together with the description, serve to explain the principles of the present disclosure.
[0021] FIG. 1 illustrates an exemplary representation of architecture of the proposed system for flood risk forecasting for disaster resilience, in accordance with an embodiment of the present disclosure.
[0022] FIG. 2 illustrates a block diagram representation of the proposed system for flood risk forecasting for disaster resilience, in accordance with an embodiment of the present disclosure.
[0023] FIG. 3 illustrates an exemplary view of a flow diagram of the proposed application for flood risk forecasting for disaster resilience, in accordance with an embodiment of the present disclosure.
[0024] FIG. 4 illustrates an exemplary view of a flow diagram of the proposed method for flood risk forecasting for disaster resilience, in accordance with an embodiment of the present disclosure.
[0025] FIG. 5 illustrates an exemplary graphical representation of the Flood Risk prediction for Suvarna River by Flood AI tool, in accordance with an embodiment of the present disclosure.
[0026] FIG. 6 illustrates an exemplary graphical representation of the Evacuation plan strategy for Flood Risks, in accordance with an embodiment of the present disclosure.
[0027] FIG. 7 illustrates an exemplary graphical representation of the resource allocation strategy, in accordance with an embodiment of the present disclosure.
[0028] FIG. 8 illustrates an exemplary graphical representation of the flood warning of the proposed model prediction vs true prediction.
[0029] FIG. 9 illustrates an exemplary photographic representation of the flood maps of satellite images.
DETAILED DESCRIPTION
[0030] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[0031] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[0032] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0033] The present invention relates to the field of flood risk forecasting and disaster resilience. More specifically, it pertains to the use of advanced machine learning and deep learning techniques, including convolutional neural networks (CNNs) and semantic segmentation models, for processing diverse data types such as historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data. The invention aims to provide real-time, adaptive flood risk predictions and assessments, supporting strategic evacuation planning and resource allocation for effective disaster management.
[0034] Flood Risk AI is a deep learning-based flood prediction system designed for timely warnings and effective disaster management. Using 20 years of historical data, GIS, and SAR imagery, and focusing on Udupi's rivers as a case study, Flood Risk AI combines numerical and topological data to predict flood probability with high accuracy. The model consists of two components: one processing numerical data, achieving prediction probabilities between 0.89 and 0.95, and another handling image-based interpretation with a location prediction IoU of 0.91. Flood Risk AI provides both flood prediction and risk assessment outputs, linked to real-time forecasting and continuous monitoring, delivering essential feedback for dynamic flood risk management and resilience-building. The program continuously updates based on real-time forecasting, ensuring adaptive, real-time insights for effective disaster response.
[0035] The system utilizes continuous learning algorithms to enhance prediction accuracy based on real-time data inputs, allowing for dynamic adjustments to forecasts. The system integrates diverse data types-historical, satellite, and hydrological-into a unified model, facilitating comprehensive flood risk assessment and improving predictive capabilities. The system implements real-time data processing to provide timely insights and alerts, enabling swift decision-making and response to imminent flood threats. The system develops adaptive resource allocation strategies that dynamically adjust based on real-time flood predictions, ensuring optimal deployment of resources during flood events.
[0036] FIG. 1 illustrates an exemplary representation of architecture of the proposed system for flood risk forecasting for disaster resilience, in accordance with an embodiment of the present disclosure.
[0037] Referring to FIG.1, a system 102 for flood risk forecasting for disaster resilience to at least one user to access a computing device. The system 102 includes a network 104, one or more computing devices 106-1, 106-2…,106-N (individually referred to as one or more computing devices 106), one or more users 108-1, 108-2…,108-N (individually referred to as one or more users 108), and a centralized server 110. The system 102 includes a processor 202 and a memory 204. The memory 204 may include a set of instructions, which when executed, causes the processor 202 to detect a plurality of traffic violations by a plurality of users. The one or more user transmission is received via one or more computing devices 106.
[0038] In an embodiment, the system 102 for flood risk forecasting for disaster resilience to users includes a processor 202 operatively coupled to a memory 204 that includes a set of instructions, which upon being executed, causes the processor 202 to provide flood risk forecasting and generate a report.
[0039] FIG. 2 illustrates a block diagram representation of the proposed system for flood risk forecasting for disaster resilience, in accordance with an embodiment of the present disclosure.
[0040] Referring to FIG. 2, an exemplary architecture 200 of the proposed system 102 is disclosed. The system 102 includes one or more processor(s) 202. Among other capabilities, the one or more processor(s) 202 are configured to fetch and execute computer-readable instructions stored in the memory 204 of the device. The memory 204 stores one or more computer-readable instructions or routines, which are fetched and executed to create or share the data units over a network service.
[0041] In an embodiment, the system 102 also includes an interface(s) 206. The interface(s) 206 facilitates communication of the user 108 with various devices or servers coupled to the user device. The interface(s) 206 also provides a communication pathway for one or more components of the user device 108. The interface 206 is configured to allow the user 108 to view reports pertaining to the flood forecast by the system 102.
[0042] In an embodiment, the processing engine(s) 208 are implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. The database 220 includes data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208.
[0043] In an embodiment, the processing engine(s) 208 can include a data processing engine 210, an image processing engine 212, an AI driven feedback unit 214, an evacuation planning engine 216, a resource allocation engine 222, an input engine 224, an output engine 226, and other module(s) 230, but not limited to the likes. The other module(s) 218 implements functionalities that supplement applications or functions performed by the system 102 or the processing engine(s) 208. The data (or database 220) serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules.
[0044] In an embodiment, the system 102 is configured to receive a plurality of data types including historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data via the input engine 224. The historical data includes past flood events, rainfall records, and river flow measurements. The satellite imagery includes Synthetic Aperture Radar (SAR) images for enhanced real-time monitoring and predictive capabilities. The GIS data includes elevation data, land use data, and infrastructure information. The topological data includes river network data and watershed boundaries. The hydrological data includes soil moisture levels, groundwater levels, and precipitation forecasts. The geospatial data includes spatial coordinates and geographic features relevant to flood risk assessment.
[0045] In an embodiment, the system 102 is configured to process numerical data using a convolutional neural network (CNN) to generate flood prediction probabilities via the data processing engine 210. The prediction probabilities range between 0.89 and 0.95. The CNN and a deep learning model are trained using supervised learning techniques on historical data.
[0046] In an embodiment, the system 102 is configured to process image-based data using a deep learning model based on semantic segmentation to detect and map flood locations via the image processing engine 212. The location prediction achieves an Intersection over Union (IoU) of 0.91.
[0047] In an embodiment, the system 102 is configured to continuously update the flood risk predictions based on real-time forecasting data to provide adaptive, real-time insights.
[0048] In an embodiment, the system 102 is configured to generate an AI-driven feedback unit 214 to continuously assess flood risks and enhance adaptive resilience. The AI-driven feedback unit 214 utilizes machine learning algorithms to continuously improve the accuracy of flood risk predictions.
[0049] In an embodiment, the system 102 is configured to support strategic evacuation planning via the evacuation planning engine 216 and adaptive resource allocation via the resource allocation engine 222 based on the real-time flood predictions. The strategic evacuation planning includes identifying safe evacuation routes and shelters based on the flood risk predictions. The adaptive resource allocation includes dynamically deploying emergency response resources such as personnel, equipment, and supplies based on real-time flood predictions.
[0050] In an embodiment, the system 102 is configured to provide output data including flood risk predictions, flood detection maps, and resource allocation strategies for effective disaster management via the output engine 226. Flood risk maps are generated to visually represent areas at risk of flooding based on the processed data. Historical flood maps are utilized to identify past flood patterns and trends for improved predictive capabilities. The output data is provided to disaster management authorities for informed decision-making and effective disaster response. Performance of the flood risk forecasting system 102 is evaluated using metrics such as prediction accuracy, IoU, response time and any combination thereof.
[0051] FIG. 3 illustrates an exemplary view of a flow diagram of the proposed application for flood risk forecasting for disaster resilience, in accordance with an embodiment of the present disclosure.
[0052] The Flood Risk AI model (300) integrates hydrological and topographical data for comprehensive flood risk analysis. A Novel Proposed Flood Risk AI integrates regression networks, effectively handling both historical and satellite imagery data for enhanced analysis and predictions. The model consists of two components: one processing numerical data, achieving prediction probabilities between 0.89 and 0.95. Another handling image-based interpretation with a location prediction IoU of 0.91. It utilizes historical flood maps to identify past flood patterns and trends. Satellite imagery (SAR) is employed to enhance real-time monitoring and predictive capabilities. This combination allows for precise flood predictions and effective disaster management strategies. The AI-driven feedback system ensures continuous assessment of flood risks, enhancing adaptive resilience. This continuous feedback loop facilitates timely interventions and improved preparedness for future flood events.
[0053] Hardware and Software Tools Used: Programming Language: Python for data processing and model implementation. GPU Processing: Utilized NVIDIA GPUs for accelerated training of machine learning models. Deep Learning Frameworks: TensorFlow for building models; Keras for simplified model training. Algorithm Training Parameters: Learning Rate: 0.001; Batch Size: 32; Epochs: 100. Data Handling: Pandas and NumPy for data manipulation; GIS tools for spatial data integration.
[0054] The approach involved two processing models organized into separate blocks. The first model is a CNN that processes numerical data to generate predictions as shown in Table 1 and Table 2.
Table 1: Overview of CNN Model Incorporating Multiple Data Types
Component Details
Data Types - Topological Data: Terrain and landscape features influencing water flow.
- Historical Data: Past flood records for model training.
- Satellite Data: High-resolution imagery for real-time flood analysis.
- Hydrological Data: Measurements including rainfall and river flow metrics.
Input Layer Reshaped numerical data based on selected data types. Input shape varies when integrating satellite imagery with numerical time series data.
Convolutional Layers - Layer 1: 32 filters, kernel size of 3, ReLU activation.
- Layer 2: 64 filters, kernel size of 3 for enhanced feature extraction.
Pooling Layers Max Pooling Layer: Reduces spatial dimensions for abstract feature representation, applied after each convolutional layer.
Dropout Layer Optional layer added post-pooling to reduce overfitting, with a dropout rate of 0.2 to 0.5.
Flatten Layer Converts pooled feature maps into a 1D vector for fully connected layers.
Fully Connected Layers - Dense Layer 1: 64 neurons, ReLU activation to capture relationships in the data.
- Dense Layer 2 (Output Layer): 1 neuron, linear activation for flood prediction.
Loss Function Mean Squared Error (MSE) for quantifying differences between predicted and actual values.
Optimizer Adam optimizer for adaptive learning rates ensuring efficient convergence.
Metrics Mean Absolute Error (MAE) and R-squared for model performance assessment during training.
Regularization Techniques L2 regularization in dense layers to prevent overfitting by discouraging large weights.
Cross-Validation 5-fold cross-validation to enhance model robustness and generalization on different dataset subsets.
Table 2: Model Summary Table
Layer Output Shape Parameters
Input Layer (10, 1) -
Conv1D Layer 1 (8, 32) 128
MaxPooling1D Layer 1 (4, 32) 0
Dropout Layer 1 (4, 32) 0
Conv1D Layer 2 (2, 64) 6,208
MaxPooling1D Layer 2 (1, 64) 0
Flatten Layer (64) 0
Dense Layer 1 (64) 4,160
Dense Layer 2 (Output) (1) 65
Total Parameters 10,553
The second model employs deep learning techniques based on semantic segmentation, which assesses pixel accuracy and maps flood detection. Both models are supervised learning systems trained on historical data as shown in Table 3.
Table 3: Semantic Segmentation Model Summary for SAR and GIS Images
Component/Layer Type Output Shape Parameters Details
Input Layer (225, 225, 1) - Accepts single-channel SAR images.
GIS Data Input (225, 225, 3) - Accepts multi-channel GIS data (e.g., elevation, land use).
Convolutional Layer 1 (223, 223, 32) 320 First convolutional layer for feature extraction.
Max Pooling Layer 1 (111, 111, 32) 0 Reduces spatial dimensions after first convolution.
Convolutional Layer 2 (109, 109, 64) 18,496 Second convolutional layer for deeper feature extraction.
Max Pooling Layer 2 (54, 54, 64) 0 Further reduces spatial dimensions.
Convolutional Layer 3 (52, 52, 128) 73,856 Extracts more complex features.
Max Pooling Layer 3 (26, 26, 128) 0 Reduces spatial dimensions again.
Dropout Layer (26, 26, 128) 0 Prevents overfitting during training.
Upsampling Layer 1 (52, 52, 128) 0 Increases spatial resolution for pixel-wise classification.
Convolutional Layer 4 (50, 50, 64) 73,792 Continues feature extraction.
Upsampling Layer 2 (109, 109, 64) 0 Recovers resolution for final output.
Convolutional Layer 5 (107, 107, 32) 18,432 Further feature extraction before output.
Output Layer (225, 225, 2) 130 Provides pixel-wise classification for flood detection.
Total Parameters 284,242 Total number of parameters across the model.
Optimizer Adam optimizer for efficient convergence.
Loss Function Categorical Cross entropy for segmentation.
Activation Function Softmax for multi-class output.

[0055] FIG. 4 illustrates an exemplary view of a flow diagram of the proposed method for flood risk forecasting for disaster resilience, in accordance with an embodiment of the present disclosure.
[0056] In an embodiment, the proposed method 400 for flood risk forecasting for disaster resilience is disclosed. At step 402, receiving, by a system 102, a plurality of data types including historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data.
[0057] At step 404, processing, by the system 102, numerical data using a convolutional neural network (CNN) to generate flood prediction probabilities.
[0058] At step 406, processing, by the system 102, the image-based data using a deep learning model based on semantic segmentation to detect and map flood locations.
[0059] At step 408, continuously updating, by the system 102, the flood risk predictions based on real-time forecasting data to provide adaptive, real-time insights.
[0060] At step 410, generating, by the system 102, an AI-driven feedback unit 214 to continuously assess flood risks and enhance adaptive resilience.
[0061] At step 412, supporting, by the system 102, strategic evacuation planning and adaptive resource allocation based on the real-time flood predictions.
[0062] At step 414, providing, by the system 102, output data including flood risk predictions, flood detection maps, and resource allocation strategies for effective disaster management.
[0063] In an exemplary embodiment, a convolutional neural network (CNN) processes numerical data to generate flood prediction probabilities, achieving prediction probabilities between 0.89 and 0.95. The CNN model is trained using supervised learning techniques on historical data. A deep learning model based on semantic segmentation processes image-based data to detect and map flood locations, achieving an Intersection over Union (IoU) of 0.91. The model assesses pixel accuracy and maps flood detection using SAR and GIS images. The system continuously updates flood risk predictions based on real-time forecasting data, providing adaptive, real-time insights. This ensures dynamic adjustments to forecasts and enhances the model's effectiveness in rapidly changing environmental conditions, such as flash floods. Flood Risk AI incorporates an AI-driven feedback system that continuously assesses flood risks and enhances adaptive resilience. This feedback loop facilitates timely interventions, improved preparedness, and continuous improvement of the system's predictive capabilities. The system supports strategic evacuation planning by identifying safe evacuation routes and shelters based on real-time flood predictions. It also develops adaptive resource allocation strategies that dynamically adjust based on real-time flood predictions, ensuring optimal deployment of resources during flood events. The system provides comprehensive output data, including flood risk predictions, flood detection maps, and resource allocation strategies, to disaster management authorities for informed decision-making and effective disaster response. Flood risk maps visually represent areas at risk of flooding based on the processed data. The performance of the flood risk forecasting system is evaluated using metrics such as prediction accuracy, IoU, and response time. This ensures continuous improvement and reliability of the system.
[0064] FIG. 5 illustrates an exemplary graphical representation of the Flood Risk prediction for Suvarna River by Flood AI tool, in accordance with an embodiment of the present disclosure.
[0065] FIG. 6 illustrates an exemplary graphical representation of the Evacuation plan strategy for Flood Risks, in accordance with an embodiment of the present disclosure.
[0066] FIG. 7 illustrates an exemplary graphical representation of the resource allocation strategy, in accordance with an embodiment of the present disclosure.
[0067] FIG. 8 illustrates an exemplary graphical representation of the flood warning of the proposed model prediction vs true prediction.
[0068] FIG. 9 illustrates an exemplary photographic representation of the flood maps of satellite images.
[0069] Referring to FIG. 5, FIG. 6, FIG. 7, FIG. 8 and FIG. 9, Flood AI is applied to the Suvarna River, focusing on flood risk prediction, strategic evacuation planning, and resource allocation. The tool's semantic mapping capabilities allow for pinpointing areas with detected flood risks, supporting a more targeted response. By integrating both historical data and real-time inputs, Flood AI effectively assesses various flood levels, aiding in immediate resource deployment and evacuation measures. In this ongoing model training, future predictive results are being optimized for proactive flood management. The Udupi River is utilized as a case study to enhance the tool's predictive accuracy and response planning.
[0070] Overall, Flood Risk AI provides a novel and effective solution for flood risk forecasting and disaster resilience, leveraging advanced technologies and methodologies to deliver accurate, real-time predictions and assessments, supporting strategic evacuation planning and resource allocation for effective disaster management.
[0071] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are comprised to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

 
, Claims:1. An AI based system (102) for flood risk forecasting for disaster resilience, the system (102) comprising:
a processor (202) operatively coupled to a memory (204), wherein the memory (204) comprises processor-executable instructions, which on execution, causes the processor (202) to:
receive a plurality of data types including historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data;
process numerical data using a convolutional neural network (CNN) to generate flood prediction probabilities, wherein the prediction probabilities range between 0.89 and 0.95;
process image-based data using a deep learning model based on semantic segmentation to detect and map flood locations, wherein the location prediction achieves an Intersection over Union (IoU) of 0.91;
continuously update the flood risk predictions based on real-time forecasting data to provide adaptive, real-time insights;
generate an AI-driven feedback unit (214) to continuously assess flood risks and enhance adaptive resilience;
support strategic EVACUATION PLANNING and adaptive resource allocation based on the real-time flood predictions;
provide output data including flood risk predictions, flood detection maps, and resource allocation strategies for effective disaster management.
2. The system (102) as claimed in claim 1, wherein the CNN and a deep learning model is trained using supervised learning techniques on historical data.
3. The system (102) as claimed in claim 1, wherein the AI-driven feedback unit (214) utilizes machine learning algorithms to continuously improve the accuracy of flood risk predictions.
4. The system (102) as claimed in claim 1, wherein flood risk maps are generated to visually represent areas at risk of flooding based on the processed data.
5. The system (102) as claimed in claim 1, wherein the strategic evacuation planning includes identifying safe evacuation routes and shelters based on the flood risk predictions.
6. The system (102) as claimed in claim 1, wherein the adaptive resource allocation includes dynamically deploying emergency response resources such as personnel, equipment, and supplies based on real-time flood predictions.
7. The system (102) as claimed in claim 1, wherein historical flood maps are utilized to identify past flood patterns and trends for improved predictive capabilities.
8. The system (102) as claimed in claim 1, wherein the output data is provided to disaster management authorities for informed decision-making and effective disaster response.
9. The system (102) as claimed in claim 1, wherein performance of the flood risk forecasting system (102) is evaluated using metrics such as prediction accuracy, IoU, response time and any combination thereof.
10. An AI based method (400) for flood risk forecasting for disaster resilience, the method (400) comprising steps of:
receiving (402), by a system (102), a plurality of data types including historical data, satellite imagery, GIS data, topological data, hydrological data, and geospatial data;
processing (404), by the system (102), numerical data using a convolutional neural network (CNN) to generate flood prediction probabilities, wherein the prediction probabilities range between 0.89 and 0.95;
processing (406), by the system (102), image-based data using a deep learning model based on semantic segmentation to detect and map flood locations, wherein the location prediction achieves an Intersection over Union (IoU) of 0.91;
continuously updating (408), by the system (102), the flood risk predictions based on real-time forecasting data to provide adaptive, real-time insights;
generating (410), by the system (102), an AI-driven feedback unit (214) to continuously assess flood risks and enhance adaptive resilience;
supporting (412), by the system (102), strategic evacuation planning and adaptive resource allocation based on the real-time flood predictions;
providing (414), by the system (102), output data including flood risk predictions, flood detection maps, and resource allocation strategies for effective disaster management.

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