EARTHQUAKE PREDICTION USING ML TECHNIQUES

Abstract

Earthquakes are among the most devastating natural disasters, often striking without warning and causing significant loss of life and infrastructure damage. The unpredictability of seismic activity poses major challenges to governments, disaster response teams, and communities worldwide. Traditional earthquake prediction methods primarily rely on geological surveys, seismographic data, and historical trends, but these approaches often lack precision and fail to provide early warnings. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer a promising alternative to improve earthquake forecasting through data-driven analysis. This project aims to develop a machine learning-based earthquake prediction system that leverages historical seismic data, geological parameters, and real-time sensor inputs to enhance early warning mechanisms.
The proposed system integrates various machine learning and deep learning techniques to analyze seismic activity patterns and predict potential earthquakes with greater accuracy. Traditional machine learning models, such as Random Forest, XGBoost, and Support Vector Machines (SVM), are employed for initial feature selection and classification. Additionally, deep learning approaches, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), are utilized for time-series analysis and pattern recognition in seismic signals. These models help detect anomalies in tectonic movements, allowing for the identification of precursors that may indicate an impending earthquake.