SAHAY: AN INTEGRATED, KERALA-FOCUSED DISASTER MANAGEMENT, EARLY-WARNING AND RESCUE COORDINATION PLATFORM

Natural disasters such as floods, landslides, and extreme rainfall require timely prediction, reliable information, effective communication, and coordinated emergency response. Recent research has explored Artificial Intelligence (AI), Machine Learning (ML), Geographic Information Systems (GIS), Internet of Things (IoT), crowdsourcing, and early-warning technologies for different aspects of disaster management. However, many existing approaches remain fragmented, focusing mainly on prediction, monitoring, mapping, or information collection rather than connecting these functions with practical emergency response. This review examines existing disastermanagement research and software technologies, with particular emphasis on Kerala-specific studies related to landslides, rainfall, floods, and real-time monitoring. Based on the identified research gaps, SAHAY is proposed as an integrated Keralafocused disaster-management and decisionsupport platform that combines real-time weather and location data, AI/ML-based risk assessment, GIS visualization, personalized alerts, citizen incident reporting, SOS, evacuation and shelter assistance, and rescue-team coordination. The proposed platform aims to bridge the gap between disaster prediction and emergency response through a unified, multi-role system.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-29
DOI
https://doi.org/10.5281/zenodo.23756907
Primary Topic
Energy and Environmental Systems
Type
preprint
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SAHAY: AN INTEGRATED, KERALA-FOCUSED DISASTER MANAGEMENT, EARLY-WARNING AND RESCUE COORDINATION PLATFORM

Jinson Devis, Aparna Prasad
3 links
Zenodo (CERN European Organization for Nuclear Research)
Energy and Environmental Systems
preprint

SAHAY: AN INTEGRATED, KERALA-FOCUSED DISASTER MANAGEMENT, EARLY-WARNING AND RESCUE COORDINATION PLATFORM

Jinson Devis, Aparna Prasad
preprint en

Abstract

Natural disasters such as floods, landslides, and extreme rainfall require timely prediction, reliable information, effective communication, and coordinated emergency response. Recent research has explored Artificial Intelligence (AI), Machine Learning (ML), Geographic Information Systems (GIS), Internet of Things (IoT), crowdsourcing, and early-warning technologies for different aspects of disaster management. However, many existing approaches remain fragmented, focusing mainly on prediction, monitoring, mapping, or information collection rather than connecting these functions with practical emergency response. This review examines existing disastermanagement research and software technologies, with particular emphasis on Kerala-specific studies related to landslides, rainfall, floods, and real-time monitoring. Based on the identified research gaps, SAHAY is proposed as an integrated Keralafocused disaster-management and decisionsupport platform that combines real-time weather and location data, AI/ML-based risk assessment, GIS visualization, personalized alerts, citizen incident reporting, SOS, evacuation and shelter assistance, and rescue-team coordination. The proposed platform aims to bridge the gap between disaster prediction and emergency response through a unified, multi-role system.

Zenodo (CERN European Organization for Nuclear Research)
Amal Jyothi College of Engineering (IN)
Industry, innovation and infrastructure, Sustainable cities and communities
Energy and Environmental Systems
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SAHAY: AN INTEGRATED, KERALA-FOCUSED DISASTER MANAGEMENT, EARLY-WARNING AND RESCUE COORDINATION PLATFORM — Jinson Devis, Aparna Prasad · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS