Kaydedildi:
| Yazar: | |
|---|---|
| Materyal Türü: | Recurso digital |
| Dil: | |
| Baskı/Yayın Bilgisi: |
Zenodo
2026
|
| Online Erişim: | https://doi.org/10.5281/zenodo.19915559 |
| Etiketler: |
Etiketle
Etiket eklenmemiş, İlk siz ekleyin!
|
İçindekiler:
- <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>