GEOSPATIAL PREDICTIVE FLOOD RESILIENCE: CONVOLUTIONAL NEURAL NETWORK-DRIVEN BLOCKCHAIN FOR EARLY FLOOD WARNING AND COMMUNITY RESPONSE IN CHENNAI, LEVERAGING SATELLITE-BASED DATA

Abstract

GEO-SPATIAL PREDICTIVE FLOOD RESILIENCE: CONVOLUTIONAL NEURAL NETWORK-DRIVEN BLOCK CHAIN FOR EARLY FLOOD WARNING AND COMMUNITY RESPONSE IN CHENNAI, LEVERAGING SATELLITE-BASED DATA This system proposes a novel and innovative implementation for flood resilience by introducing satellite data (Dindi), Convolutional Neural Networks (CNN) (and Blockchain) for early warning, flooding forecasting, and prompt community based response in Chennai. It uses satellite imagery and IoT (Internet of Things) sensors to acquire environmental data in real-time—tracking weather patterns, river levels and soil moisture. Geospatial data To predict flood risks faster and more accurately, they used the trained CNN model to analyze the geospatial data referenced against a database of historical flood events. With real-time flood risk data that is always improving the system provides dynamic risk assessment. Per the predicted data, early flood warnings are automatically provided to the authorities and community which in result helps with evacuation & precaution measures. Reasons for using blockchain technology are secure, tamperproof storage of historical data and the capability to enable decentralized response coordination with smart contracts that can automate alert mechanisms when predefined criteria have been met and automatically distribute resources. The platform delivers a unified disaster management approach to ensure effective, coordinated efforts amongst government institutions and first responders in collaboration with local communities, as well as post-event analytics for resource optimization (following disasters). Being data-driven, free from centralized solutions, and at the same time decentralized makes this entirely scalable solution an effective tool for reducing flood related damages in Chennai.

Specification

Description:GEO-SPATIAL PREDICTIVE FLOOD RESILIENCE: CONVOLUTIONAL NEURAL NETWORK-DRIVEN BLOCK CHAIN FOR EARLY FLOOD WARNING AND COMMUNITY RESPONSE IN CHENNAI, LEVERAGING SATELLITE-BASED DATA

Technical Field
[0001] The embodiments herein generally relate to a method for geo-spatial predictive flood resilience: convolutional neural network-driven block chain for early flood warning and community response in chennai, leveraging satellite-based data.

Description of the Related Art
[0002] Field of Invention: Climate Change
[0003] Background Art including citations of prior art: There are no inventions existing similar to the digital displays which varies with respect to the people having refractive errors.
[0004] Objective of invention (the invention's objectives and advantages, or alternative embodiment's of the invention): To enhance the accessibility and usability of digital displays for individuals with refractive vision difficulties, without the need for corrective eyewear such as spectacles/lenses/eye surgery.
SUMMARY
[0005] This system proposes a novel and innovative implementation for flood resilience by introducing satellite data (Dindi), Convolutional Neural Networks (CNN) (and Blockchain) for early warning, flooding forecasting, and prompt community based response in Chennai. It uses satellite imagery and IoT (Internet of Things) sensors to acquire environmental data in real-time-tracking weather patterns, river levels and soil moisture. Geospatial data To predict flood risks faster and more accurately, they used the trained CNN model to analyze the geospatial data referenced against a database of historical flood events. With real-time flood risk data that is always improving the system provides dynamic risk assessment. Per the predicted data, early flood warnings are automatically provided to the authorities and community which in result helps with evacuation & precaution measures. Reasons for using blockchain technology are secure, tamperproof storage of historical data and the capability to enable decentralized response coordination with smart contracts that can automate alert mechanisms when predefined criteria have been met and automatically distribute resources.
[0006] The platform delivers a unified disaster management approach to ensure effective, coordinated efforts amongst government institutions and first responders in collaboration with local communities, as well as post-event analytics for resource optimization (following disasters). Being data-driven, free from centralized solutions, and at the same time decentralized makes this entirely scalable solution an effective tool for reducing flood related damages in Chennai.

, Claims:1. A satellite and IoT sensor based early flood warning system and community response model in Chennai, which uses CNN model for prediction, block chain based system for alerting the users and finally end user interface to issue warnings.
2. A method including collecting real-time environmental data from satellite and IoT sensors followed by analysing this information using a trained CNN model in order to predict flood risks at Chennai, monitoring and updating predictions for flood risk, issuing early warnings to communities and authorities, applying blockchain based system where decentralized response coordination can be done securely.
3. Convolutional Neural Network (CNN) model for flood risk prediction: Involves geospatial data to train a CNN model which will facilitate in accurate determination as well as future flood forecasting areas.
4. Blockchain- Secure data management & decentralized response coordination using a blockchain based system with smart contracts to allow for stakeholders collaborations. Selected Licenses.
5. A system to provide the communities and authorities with early flood warnings, given the time-sensitive predictions by the CNN model.
6. A system to coordinate community-generated flood responses, in which the blockchain tracks decentralized decision-making and resource allocation in those times of emergency.
7. A Real-time monitoring and Updating System of Flood Risk Information for Real Time Imapact based forecasting.
8. A system which help in collecting real-time data from the environment ( satellite and IoT sensors), Granting a better perspective of the flood risk factors located around Chennai.
9. This section focuses on principles eight (knowledge to action and effective investment) and nine (post -evaluation design learning from past flood experiences for better disaster preparedness/response).
10. An early flood warning and community response system integrating satellite-based data and advanced technologies for Chennai area to improve resilience of the society during extreme events of floods.

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