AI for Disaster Response: Developing Intelligent Systems for Real-Time Emergency Management
T.K. Lakshmi, Naresh Kumar Mangalapu, Mary Fathima Anuja Joseph Swaminathan, S. Chandravadhana, R. Pari, T. Ranjana · 2025
The research introduces the design of an AI-based disaster response system that subscribes to enhance real-time emergency management. Manual processing, slow communication and siloed data sources are common downsides of traditional-approach systems, which ultimately translate into slower response times or insufficient resource spend. The system deals with them by exploiting ML, predictive analytics, and automatic decision-making so that these can consolidate pre-existing real-time information from social media, satellite images or IoT sensors. Several experiments show better-than-previous: response times decrease from an average of 11 hours to 3.6 and prediction accuracy increased from 62.5% up to cover %85 Average efficiency also improved including +23% in resource allocation. The innovative solution shifts disaster response from being entirely reactive to proactive, creating faster and more informed responses during times of crisis, thereby assuring public safety for all in the face of calamities.