Comparative Analysis of Machine Learning Models for Earthquake Prediction: Evaluating LSTM and Logistic Regression Using Seismic and Metadata Features

Abstract

Being able to predict an earthquake in advance ensures that meaningful steps are taken to mitigate a disaster and manage its impacts on human life and infrastructure. The aim of this research is to utilize machine learning algorithms to fully automate the detection of early signs of a potential earthquake using real-time streaming data for seismic analysis. The study compares the performance of modern deep learning techniques (LSTM, CNN) with that of older, more established statistical techniques like Logistic regression, KNN, and Decision trees for the prediction of seismic activities. Unlike the traditional method which relies only on primary wave signals as the sole input, it integrates associated earthquake metadata to enhance forecast precision and expedite alarm issuance. Also included in this work is an automated alert system that has the capability to autonomously and instantly generate messages and dispatch them through a specified communication port, thereby guaranteeing that sensitive populations are alerted in a timely and effective manner. The experiments illustrate that machine learning techniques are capable of accurately detecting the signatures of seismic activity and providing prompt reliable warnings. This research also highlights the importance of integrating artificial intelligence-based prediction models to disaster management systems to improve the level of response to emergencies. The emerging data-driven systems for issuing early warning alerts can enhance earthquake prediction and disaster management, thus improving the current position of technology. This research proves its feasibility.