PRAKRUTHI PRALAYAM: AN INTELLIGENT DISASTER PREDICTION AND EMERGENCY MANAGEMENT SYSTEM FOR INDIA

Project Title: Prakruthi Pralayam: A Data-Driven Framework for Disaster Response and Risk MitigationThis project presents a comprehensive disaster management system tailored for the Indian geographical context. Titled "Prakruthi Pralayam" (Nature's Catastrophe), the study focuses on integrating real-time data analytics with emergency response protocols. The framework utilizes Python-based data pipelines to aggregate multi-source disaster data, providing a centralized dashboard for situational awareness. By analyzing historical disaster patterns and current environmental metrics, the project aims to minimize response latency and optimize resource allocation during floods, cyclones, and other natural calamities. Key features include automated risk assessment, spatial data visualization, and an integrated communication module for disaster relief coordination.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-01
DOI
https://doi.org/10.5281/zenodo.19940682
Primary Topic
Computational Physics and Python Applications
Type
article
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PRAKRUTHI PRALAYAM: AN INTELLIGENT DISASTER PREDICTION AND EMERGENCY MANAGEMENT SYSTEM FOR INDIA

Andhra University
Zenodo (CERN European Organization for Nuclear Research)
Computational Physics and Python Applications
article

PRAKRUTHI PRALAYAM: AN INTELLIGENT DISASTER PREDICTION AND EMERGENCY MANAGEMENT SYSTEM FOR INDIA

Andhra University
article en

Abstract

Project Title: Prakruthi Pralayam: A Data-Driven Framework for Disaster Response and Risk MitigationThis project presents a comprehensive disaster management system tailored for the Indian geographical context. Titled "Prakruthi Pralayam" (Nature's Catastrophe), the study focuses on integrating real-time data analytics with emergency response protocols. The framework utilizes Python-based data pipelines to aggregate multi-source disaster data, providing a centralized dashboard for situational awareness. By analyzing historical disaster patterns and current environmental metrics, the project aims to minimize response latency and optimize resource allocation during floods, cyclones, and other natural calamities. Key features include automated risk assessment, spatial data visualization, and an integrated communication module for disaster relief coordination.

Zenodo (CERN European Organization for Nuclear Research)
Climate action
Openalex Percentile: Top 3%
Computational Physics and Python Applications
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