AI-powered disaster management System
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Zenodo
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866902230480715776 |
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| author | Mrs. Meenakshi Simha |
| author_facet | Mrs. Meenakshi Simha |
| contents | <p>An AI-powered disaster management system is designed to predict, detect, and mitigate the impact of natural disasters, with an initial focus on earthquake response. This system leverages Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Generative AI (GenAI) to provide comprehensive disaster lifecycle management. In the pre-disaster phase, the system predicted the likelihood and severity of earthquakes using ML models trained on historical and real-time environmental data. During a disaster, it enables real-time detection and visualization through an intuitive map-based interface (using Leaflet.js), while also sending location-based alerts and AI-generated personalized rescue plans via SMS. These response plans consider real-time geographic and infrastructural factors to optimize evacuation strategies. After a disaster, the system automates damage assessment by aggregating critical data from online sources to generate detailed evaluation reports for future analysis and recovery planning. Built with a React frontend and Python Flask backend, the response system integrates advanced AI techniques to enhance disaster preparedness, response efficiency, and resilience. Future work will aim to extend these capabilities to a broader range of natural disasters.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15365208 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-powered disaster management System Mrs. Meenakshi Simha <p>An AI-powered disaster management system is designed to predict, detect, and mitigate the impact of natural disasters, with an initial focus on earthquake response. This system leverages Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Generative AI (GenAI) to provide comprehensive disaster lifecycle management. In the pre-disaster phase, the system predicted the likelihood and severity of earthquakes using ML models trained on historical and real-time environmental data. During a disaster, it enables real-time detection and visualization through an intuitive map-based interface (using Leaflet.js), while also sending location-based alerts and AI-generated personalized rescue plans via SMS. These response plans consider real-time geographic and infrastructural factors to optimize evacuation strategies. After a disaster, the system automates damage assessment by aggregating critical data from online sources to generate detailed evaluation reports for future analysis and recovery planning. Built with a React frontend and Python Flask backend, the response system integrates advanced AI techniques to enhance disaster preparedness, response efficiency, and resilience. Future work will aim to extend these capabilities to a broader range of natural disasters.</p> |
| title | AI-powered disaster management System |
| url | https://doi.org/10.5281/zenodo.15365208 |