Ai-Driven Disaster Prediction And Intelligent Response System

Abstract

The increasing frequency and intensity of natural disasters such as floods, earthquakes, and cyclones necessitate the development of intelligent and proactive disaster management systems. This paper presents an AI-Driven Disaster Prediction and Intelligent Response System that integrates Machine Learning (ML), Large Language Models (LLMs), and Computer Vision techniques to enhance disaster prediction, situational awareness, and emergency response.
The proposed system leverages historical and environmental datasets to estimate disaster risks based on spatial and temporal inputs. Multiple ML models, including ensemble techniques, are utilized to improve prediction robustness, while real-time external data sources are incorporated to enhance contextual awareness. A Large Language Model is employed to interpret prediction outputs and generate human-readable insights along with safety recommendations.
To support emergency response, the system integrates a YOLO-based computer vision module for analyzing disaster images to detect affected individuals, infrastructure damage, and hazardous conditions. A conversational AI chatbot enables real-time interaction, while speech-to-text and text-to-speech modules provide voice-based accessibility. Additionally, a multilingual interface improves usability across diverse user groups.
The system further includes an interactive map-based visualization module for identifying disaster-prone regions and a cloud-based database for storing prediction history and user interactions. Experimental evaluation indicates effective disaster risk estimation and reliable object detection performance in simulated rescue scenarios. Overall, the proposed system offers a scalable and integrated framework for intelligent disaster management.