Disaster Management and Earthquake /Tsunami Prediction System Using Machine Learning and Deep Learning

Abstract

Humanity experiences its most destructive events through natural disasters which include earthquakes and tsunamis. The events happen unexpectedly and result in extensive human deaths and material destruction and they provide emergency teams with insufficient time for rescue operations. The traditional disaster management systems require historical analysis and manual monitoring and they use alert systems which operate according to predefined rules. The methods show limited performance because they struggle to handle the intricate data which seismic sensors and satellites and ocean monitoring systems produce. The research project introduces a disaster management system which uses Machine Learning and Deep Learning methods as its core technology. The system acquires real-time data through continuous monitoring of seismic sensors GPS stations ocean buoys satellites and weather stations. The advanced ML and DL models examine the data to find concealed patterns and identify unusual behavior and forecast earthquake and tsunami events. The system starts disaster detection through automatic early warning systems which notify emergency personnel while it assists in creating evacuation strategies. The system uses prediction based on data to create automated response systems which enhance prediction results while decreasing response durations and reducing human and financial damages. The proposed solution aims to provide a smart, scalable, and reliable framework for modern disaster management.