Smart Disaster Response System using IoT and AI based Decision Making Algorithm

Abstract

Natural disasters such as floods, earthquakes, and wildfires are increasingly frequent and unpredictable, often resulting in massive loss of life and property. Traditional disaster response systems lack speed and adaptability. This paper proposes a smart disaster management system powered by Artificial Intelligence (AI) and Internet of Things (IoT) sensors. It focuses on how real-time environmental data collected from distributed sensors can be processed by AI algorithms to predict disasters, issue early warnings, and coordinate response operations. The system architecture includes a layered approach: IoT-based data collection, AI-based decision making, and real-time communication with emergency services. Applications in flood monitoring, wildfire alerts, and earthquake response are discussed. Challenges such as sensor reliability, data overload, and privacy are addressed. The paper also outlines future possibilities using edge AI and smart city integration. This research supports a shift toward more predictive, adaptive, and coordinated disaster management.

Introduction

Summary: AI-IoT Framework for Disaster Management

With increasing climate volatility and urbanization, natural disasters are more frequent and destructive. Traditional disaster management systems are reactive and inefficient. This paper proposes an AI-IoT-based framework for early detection, prediction, and coordinated response.

Key Components:

• IoT Sensors: Monitor environmental conditions (e.g., tremors, water levels, temperature, air quality) in real time.

• AI Algorithms: Analyze data to predict disasters, assess risk levels, and optimize responses.

• Communication Layer: Issues alerts via mobile apps, public sirens, dashboards, and drones.

Applications:

• India: Google’s AI-based flood prediction system.

• California: AI-powered wildfire detection with ALERTWildfire.

• Japan: Earthquake early warning through seismic sensors and AI.

Advantages:

• Enables early warning, automation, scalability, and data-driven planning.

• Supports simultaneous monitoring of multiple hazards.

Challenges:

• Sensor and network reliability, AI accuracy, privacy concerns, and high deployment costs.

Future Directions:

• Edge AI processing, autonomous drones, AR guidance tools, satellite integration, and real-time disaster simulations.

Conclusion

The integration of AI and IoT in disaster management offers a transformative solution for mitigating the impact of natural hazards. By enabling predictive analytics, real-time response, and data-informed recovery efforts, this technology has the potential to revolutionize emergency management globally. As connectivity and AI capabilities expand, such systems will become more accessible, accurate, and indispensable for modern societies.

References

[1] Patel, M., & Sinha, R. (2021). IoT-Based Flood Detection Systems: A Review. International Journal of Smart Technologies, 9(3), 134–139. [2] Sharma, K., & Dubey, S. (2020). Machine Learning Models for Earthquake Detection. Journal of Earth Systems, 7(2), 98–104. [3] Kumar, R., et al. (2022). Real-Time AI Prediction in Disaster Management. AI & IoT Journal, 11(1), 55–64. [4] Das, A., & Rathi, V. (2023). Smart Cities and AI-Driven Disaster Coordination. Journal of Urban Safety Technology, 5(4), 89–96. [5] World Bank. (2020). Leveraging Technology for Disaster Risk Management in South Asia. [Online]. Available: https://www.worldbank.org [6] UNDRR (United Nations Office for Disaster Risk Reduction). (2022). Technology for Disaster Risk Reduction. [Online]. Available: https://www.undrr.org [7] Google AI Blog. (2023). Advancing Flood Forecasting in India. [Online]. Available: https://ai.googleblog.com [8] ALERTWildfire Project. (2022). Real-Time Wildfire Detection with AI and IoT Cameras. [Online]. Available: https://www.alertwildfire.org [9] National Institute of Disaster Management (India). (2021). IoT in Disaster Management: White Paper. Ministry of Home Affairs.

Copyright

Copyright © 2025 M L Sharma, Sunil Kumar, Namita Gupta, Amit Raj, Vansh Aggarwal, Manan Lamba, Aamin Sadiq, Lokesh Saini. 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.