Disaster Management System: A Machine Learning Approach to Forecasting Floods and Earthquakes

Abstract: Natural disasters, particularly floods and earthquakes, pose significant threats to human life, infrastructure, and the environment. Timely and accurate prediction of such events can greatly enhance disaster preparedness and response efforts, reducing their devastating impact. This project presents an AI-Driven Disaster Prediction System that leverages machine learning algorithms to forecast the occurrence and intensity of floods and earthquakes. By analyzing key environmental and geophysical parameters such as rainfall patterns, river water levels, seismic activity, and soil composition, the system can provide early warnings and improve decision-making processes for disaster management agencies.The flood prediction component integrates historical weather data, topographical information, and water flow metrics, while the earthquake prediction model utilizes seismic activity data, fault line mapping, and ground vibration readings. Through data pre-processing, feature selection, and model training using machine learning techniques like regression models, decision trees, and time-series analysis, the system aims to predict disaster events with high accuracy.The ultimate goal of this AI-based system is to develop a scalable, realtime solution that empowers communities and governments with advanced disaster forecasting capabilities.