Machine Learning-Based Earthquake Emergency Evacuation System

Abstract

The Estimation in Earthquake Early Warning system is designed to predict and analyze earthquake-related parameters using Machine Learning techniques. The primary objective of the system is to provide an efficient and accurate early warning mechanism that can help reduce the impact of earthquakes on human life and infrastructure. The system collects seismic and environmental data, processes it using data mining and predictive algorithms, and generates warning predictions based on earthquake intensity and probability.The proposed model utilizes advanced Machine Learning algorithms to analyze historical earthquake datasets and identify hidden patterns associated with seismic activities. Various parameters such as magnitude, depth, latitude, longitude, and time are considered for prediction. The system improves prediction accuracy through preprocessing, feature extraction, and model training techniques. A user-friendly web-based interface developed using Django allows users and administrators to monitor predictions, visualize analytical reports, and manage earthquake-related data efficiently.The proposed approach offers advantages such as faster prediction, improved reliability, reduced manual analysis, and real-time monitoring capabilities. By providing early warnings and predictive insights, the system aims to support disaster management authorities and enhance public safety measures during seismic events.