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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREMWP49371

A Comprehensive Disaster Management Framework with Integrated Prediction, Response, and Rehabilitation Strategies

Stream/Area:

-

Subject:

-

Paper ID:

IJSREMWP49371

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AB

Amal Biju1

AB

Aswin Biju2

MPA

M P Arjunkrishna3

RVS

Roshan Varghese Sam4

TA

Tinimol Andrews5

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Abstract

As natural disasters become more frequent and in- tense, leveraging advanced technologies for disaster management and prediction is crucial. Natural disasters such as earthquakes and floods have a devastating impact on communities, necessi- tating efficient disaster management solutions. This framework presents an advanced disaster management platform that in- tegrates early warning systems, predictive models, and post- disaster rehabilitation strategies. Leveraging machine learning and geospatial data, the platform forecasts potential threats, issues timely alerts, and optimizes resource allocation in relief camps. Additionally, it facilitates seamless communication be- tween first responders and relief centers while supporting the rehabilitation of displaced individuals. By enhancing disaster preparedness and response, this platform aims to mitigate the impact of natural disasters and improve resilience in affected regions.

KEYWORDS

Machine learning Disaster Prediction

Cite This Article

Amal Biju1, Aswin Biju2, M P Arjunkrishna3, Roshan Varghese Sam4, Tinimol Andrews5, "A Comprehensive Disaster Management Framework with Integrated Prediction, Response, and Rehabilitation Strategies," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025.

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Publication Date

5/28/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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