Big Data-Driven Disaster Management and Early Warning System for Natural Calamities

Abstract

Big data-driven disaster management and early warning systems have emerged as vital tools in predicting, monitoring, and mitigating the effects of natural calamities such as floods, earthquakes, cyclones, and droughts. This study explores the integration of big data analytics, real-time sensor networks, and artificial intelligence to enhance the efficiency and accuracy of disaster prediction and response mechanisms. By processing vast volumes of dynamic data from satellites, weather sensors, social media, and IoT devices, the system facilitates real-time risk assessment and early warnings. Predictive models built using machine learning algorithms help identify potential disaster zones and optimize resource allocation for relief operations. Furthermore, data visualization and decision-support dashboards enable authorities to take informed, timely actions to minimize human and economic losses. The research highlights how data interoperability, cloud infrastructure, and automated alert systems can collectively strengthen national and regional resilience to natural disasters. Ultimately, the adoption of big data technologies transforms traditional disaster management frameworks into proactive, data-smart systems capable of saving lives and sustaining communities. Big data analytics combined with artificial intelligence and mathematical modeling enables a shift from reactive to proactive disaster management by simulating disaster scenarios and predicting risks based on historical and real-time data, improving preparedness and mitigation strategies. Integration of diverse data sources including satellite imagery, sensor networks, social media, GPS data, and crowdsourced information provides comprehensive situational awareness, allowing rapid detection, risk assessment, and response coordination during natural calamities.

Introduction

Big data–driven disaster management systems use vast volumes of real-time and historical data to improve disaster preparedness, response, and recovery. By integrating data from satellites, IoT sensors, weather stations, and social media, and applying AI, machine learning, and predictive analytics, these systems provide accurate early warnings and actionable insights. This enables authorities to anticipate risks, optimize resource allocation, and communicate timely alerts—significantly reducing loss of life and property.

AI and Advanced Technologies in Early Warning Systems

Modern early warning systems (EWS) rely on deep learning models such as LSTM, RNN, CNN, and transformer-based architectures to analyze multimodal data and improve forecasting accuracy. These technologies enhance real-time monitoring, disaster prediction, and decision-making compared to traditional fragmented systems.

The SACHET System

The paper introduces SACHET, a real-time, location-aware disaster alert mobile application developed by the National Disaster Management Authority (NDMA) and Centre for Development of Telematics (C-DOT). Launched nationwide in August 2021, SACHET integrates verified data from the India Meteorological Department (IMD) and NDMA to deliver reliable disaster alerts.

Key features include:

• Use of the Common Alerting Protocol (CAP) for standardized alerts

• Geo-fenced location-based SMS and cell broadcast technology

• Multi-channel dissemination (mobile alerts, TV, radio, social media, railway displays, satellite communication)

• Alerts in multiple regional languages

• Secure coordination between alert-generating and alert-authorizing agencies

Since launch, SACHET has issued over 30,000 hazard-specific alerts via billions of geo-targeted SMS messages, improving evacuation speed and response efficiency.

Problem Statement

Traditional systems suffer from delayed detection, poor data integration, limited geo-targeting, rumor spread, insufficient sensor coverage, privacy concerns, and skill shortages—particularly in developing regions. These limitations increase disaster vulnerability.

Literature Review Insights

Conventional alert systems (TV, radio, SMS) lack real-time precision and contextual guidance. Existing mobile applications often face usability issues, delayed updates, and limited localization. International systems demonstrate better integration but lack adaptability to India’s framework. There remains a gap in unified platforms combining real-time verified data, geo-targeting, user-friendly design, and emergency resource mapping—addressed by SACHET.

Methodology and Global Context

The World Meteorological Organization defines effective EWS through four pillars: risk knowledge, monitoring and forecasting, communication, and response capability. Studies show EWS can reduce fatalities eightfold and cut economic losses by up to 30% with 24-hour notice. However, many countries still lack adequate systems. The UN’s “Early Warnings for All” initiative aims for global coverage by 2027.

SACHET serves as a model of a mobile-first, unified public warning system that replaces outdated, fragmented channels.

Beyond Alerts: Financial Resilience

The paper emphasizes that early warnings must be paired with financial preparedness. Mechanisms such as:

• Forecast-based financing (FbF)

• Contingent lines of credit (CLOC)

enable vulnerable populations to act before disasters strike, strengthening overall resilience.

Results

SACHET operates as a nationwide CAP-based platform integrating IMD and other official agencies. It delivers geo-targeted alerts via SMS, mobile apps (Android/iOS), browser notifications, RSS feeds, and satellite systems. The system enhances last-mile connectivity and supports preparedness through weather forecasts, safety guidelines, and helpline access across all Indian states and union territories.

Conclusion

This study presented SACHET, a real-time, location-aware disaster alert mobile application aimed at improving the dissemination of critical information during natural disasters. The proposed system addresses key limitations of existing disaster alert mechanisms, such as delayed notifications, lack of precise geo-targeting, and absence of integrated emergency support features. By utilizing verified data from official government agencies such as the India Meteorological Department (IMD) and the National Disaster Management Authority (NDMA), the system ensures the reliability and accuracy of disaster alerts. The integration of cloud-based services, location-based technologies, and push notification mechanisms enables timely delivery of alerts to affected users. Additionally, features such as emergency resource mapping and disaster-specific safety guidelines enhance user preparedness and response capability. The conceptual design demonstrates that the proposed system is feasible, scalable, and effective in improving disaster communication and public safety. SACHET has the potential to significantly reduce response time and bridge the gap between disaster management authorities and citizens. Future enhancements may include multi- language support, predictive analytics, and wider geographic coverage to further strengthen the system’s impact.

References

[1] National Disaster Management Authority (NDMA),Government of India, “Guidelines for Disaster Management,” Available: https://www.ndma.gov.in [2] India Meteorological Department (IMD), “Weather Forecasting and Disaster Warnings,” Government of India, Available: https://mausam.imd.gov.in [3] Federal Emergency Management Agency (FEMA), “FEMA Mobile Application,” U.S. Department of Homeland Security, Available: https://www.fema.gov [4] Google Developers, “Google Maps Platform Documentation,” Available: https://developers.google.com/maps [5] 2009 Global Assessment Report on Disaster Reduction: Thematic Progress Review Sub-Component on Early Warning Systems. Available online: https://www.preventionweb.net/english/hyogo/gar/2011/en/bgdocs/GAR-2009/background_papers/Chap5/thematic-progress-reviews/WMO-early-warning-systems.doc (accessed on 5 February 2024). [6] Early Warning Systems for Improving Food Security in East and Southern Africa. Availabel online: https://documents1.worldbank.org/curated/en/104401555444833726/pdf/Early-Warning-Systems-for Improving- Food-Security-in-East-and-Southern-Africa.pdf (accessed on 4 February 2024). [7] Disaster Management Operations–Big Data Analytics to Resilient Supply Networks. Available online: http://euroma2017.eiasm.org/userfiles/HKJGLML_GDFJMK_AW7YG3VT.pdf (accessed on 5 February 2024).

Copyright

Copyright © 2026 Nagulapalli Venkatesh, Madishetti Krishna Kaarthik, Mohmad Ashad, Konda Vignesh, B. Mamatha, Dr. B. Venkataramana. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.