An Adaptive AI-Driven Multi-Hazard Prediction and Early Alert Framework for Real-Time Emergency Response Using Sensor Fusion and Deep Learning Models

Kavya Sree K., Ankit Kumar, P. Mathiyalagan, M. Vineesha, Kartheeswari M., Syed Hauider Abbas

2025

Abstract

Rapid, accurate and smart response is essential for any disaster management system to minimize life and property loss. Research approaches that have addressed real-time simulation have been single-disaster focused or have only provided non-real-time testing or little or no real-time interface. In order to circumvent these limitations, this work suggests an adaptive AI-powered multi-hazard prediction and early warning system via sensor ensemble and deep learning capabilities. Utilizing information from Internet of Things (IoT) sensors, satellite imagery, weather stations and drones, the platform supports real-time detection and forecasting of a range of disasters, including floods, earthquakes, forest fires and cyclones. The architecture uses an automated deep learning pipeline combined with lifelong learning for updating environment dynamics. It also offers emergency response and public alert dissemination accurately by scalable deployment at edge-cloud. The model is tested with two real-world datasets of disaster situations and compared to approaches based on traditional systems, showing higher precision in providing alerts to the affected public, lower response time, and fault tolerance to the operation in different regions. It is this model that seeks to shift disaster management from response to recovery.

Paper Citation

in Harvard Style

K. K., Kumar A., Mathiyalagan P., Vineesha M., M. K. and Abbas S. (2025). An Adaptive AI-Driven Multi-Hazard Prediction and Early Alert Framework for Real-Time Emergency Response Using Sensor Fusion and Deep Learning Models. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 522-529. DOI: 10.5220/0013868600004919

in Bibtex Style

@conference{icrdicct`2525,
author={Kavya K. and Ankit Kumar and P. Mathiyalagan and M. Vineesha and Kartheeswari M. and Syed Abbas},
title={An Adaptive AI-Driven Multi-Hazard Prediction and Early Alert Framework for Real-Time Emergency Response Using Sensor Fusion and Deep Learning Models},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={522-529},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013868600004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - An Adaptive AI-Driven Multi-Hazard Prediction and Early Alert Framework for Real-Time Emergency Response Using Sensor Fusion and Deep Learning Models
SN - 978-989-758-777-1
AU - K. K.
AU - Kumar A.
AU - Mathiyalagan P.
AU - Vineesha M.
AU - M. K.
AU - Abbas S.
PY - 2025
SP - 522
EP - 529
DO - 10.5220/0013868600004919
PB - SciTePress