| _version_ | 1866901965911359488 |
|---|---|
| author | Ms. Urmila A. Chavan and Ms. Priya P. Shenoy |
| author_facet | Ms. Urmila A. Chavan and Ms. Priya P. Shenoy |
| contents | <p class="MsoNormal">Effective disaster management requires not only immediate response mechanisms but also<span> </span>intelligent predictive systems that can anticipate potential hazards and alert communities in<span> </span>advance. The proposed project, “Disaster Management System Using Machine Learning,” aims to develop a smart and automated framework capable of analyzing real-time and<span> </span>historical disaster-related data to provide early warnings and decision support. The system<span> </span>leverages machine learning algorithms to process and interpret various data sources such as<span> </span>weather parameters, seismic activity, satellite imagery, and geographical information,<span> </span>enabling accurate prediction of disasters like floods, earthquakes, and cyclones.</p> <p class="MsoNormal">The integration of data analytics, prediction models, and web-based visualization allows<span> </span>authorities and users to monitor conditions dynamically and take preventive measures before<span> </span>disasters escalate. This proactive approach not only enhances situational awareness and<span> </span>emergency preparedness but also assists in resource allocation, evacuation planning, and<span> </span>post-disaster recovery. By combining predictive intelligence with responsive architecture, the<span> </span>system contributes to building a resilient, technology-driven disaster management ecosystem<span> </span>that minimizes human and economic losses, promotes sustainability, and strengthens<span> </span>community safety.</p> <p class="MsoNormal">Keywords: Disaster Management, Machine Learning, Predictive Analytics, Early Warning System,<span> </span>Environmental Monitoring, Risk Assessment, Data-driven Forecasting, Emergency Response,<span> </span>Web-based Application, Resilient Infrastructure, Disaster Preparedness.</p> <p class="MsoNormal"> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19915559 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DISASTER MANAGEMENT SYSTEM USING MACHINE LEARNING ANALYTICAL RESEARCH Ms. Urmila A. Chavan and Ms. Priya P. Shenoy <p class="MsoNormal">Effective disaster management requires not only immediate response mechanisms but also<span> </span>intelligent predictive systems that can anticipate potential hazards and alert communities in<span> </span>advance. The proposed project, “Disaster Management System Using Machine Learning,” aims to develop a smart and automated framework capable of analyzing real-time and<span> </span>historical disaster-related data to provide early warnings and decision support. The system<span> </span>leverages machine learning algorithms to process and interpret various data sources such as<span> </span>weather parameters, seismic activity, satellite imagery, and geographical information,<span> </span>enabling accurate prediction of disasters like floods, earthquakes, and cyclones.</p> <p class="MsoNormal">The integration of data analytics, prediction models, and web-based visualization allows<span> </span>authorities and users to monitor conditions dynamically and take preventive measures before<span> </span>disasters escalate. This proactive approach not only enhances situational awareness and<span> </span>emergency preparedness but also assists in resource allocation, evacuation planning, and<span> </span>post-disaster recovery. By combining predictive intelligence with responsive architecture, the<span> </span>system contributes to building a resilient, technology-driven disaster management ecosystem<span> </span>that minimizes human and economic losses, promotes sustainability, and strengthens<span> </span>community safety.</p> <p class="MsoNormal">Keywords: Disaster Management, Machine Learning, Predictive Analytics, Early Warning System,<span> </span>Environmental Monitoring, Risk Assessment, Data-driven Forecasting, Emergency Response,<span> </span>Web-based Application, Resilient Infrastructure, Disaster Preparedness.</p> <p class="MsoNormal"> </p> |
| title | DISASTER MANAGEMENT SYSTEM USING MACHINE LEARNING ANALYTICAL RESEARCH |
| url | https://doi.org/10.5281/zenodo.19915559 |