AI for Disaster Response: Predictive Models for Climate and Risk Management
Author : Dr. K Arutchelvan
Abstract :
As climate change accelerates the frequency and intensity of natural hazards, traditional static risk management frameworks are failing to keep pace. This paper introduces "ResiliNet," a novel multi-modal Deep Learning framework designed for hyper-local disaster prediction and dynamic resource allocation. Unlike existing unimodal systems that rely solely on meteorological data, ResiliNet integrates satellite synthetic aperture radar (SAR) imagery, IoT sensor streams, and real-time social media sentiment analysis via a Hybrid Early-Late Fusion architecture. Furthermore, we propose a Multi-Agent Reinforcement Learning (MARL) control layer for optimizing humanitarian logistics under uncertainty, explicitly encoding "equity" as a reward function to mitigate algorithmic bias. We validate this framework through a simulated cascading disaster scenario (wildfire triggering flash floods), demonstrating a 14% improvement in evacuation lead time and a 22% reduction in unserved demand compared to baseline heuristic models. This research argues that the future of disaster response lies not merely in prediction accuracy, but in the sociotechnical alignment of algorithmic objectives with humanitarian values.
Keywords :
Disaster Response, Deep Learning, Multi-Modal Data Fusion, Reinforcement Learning, Climate Risk, Algorithmic Equity, Remote Sensing.
