Earthquake Prediction Depending on Deep And Machine Learning Approaches: Comparative Study

Abstract
Earthquake prediction remains one of the most challenging tasks in geophysical science due to the complex and nonlinear nature of seismic activity. Traditional statistical and geophysical models struggle to provide accurate short-term or medium-term predictions. With the rise of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), models have shown promising capabilities in analyzing seismic signals, historical earthquake catalogs, and geospatial sensor data. This paper provides a comparative study of widely used ML techniques, such as Support Vector Machines (SVM), Random Forests (RF), k-Nearest Neighbors (k-NN), Decision Tree (DT), Logistic Regression (LR), Gaussian Naive Bayes (NB), and Stochastic Gradient Descent (SGD) against the proposed hybrid Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM) model with feature selection technique. Using two of the publicly available seismic datasets (the Earthquake dataset:from 1990-2023 and Turkey Earthquakes (1915-2021)), these models are compared across several metrics, including prediction accuracy, sensitivity to rare high-magnitude events, computational efficiency, and real-time feasibility. Experimental results show that while traditional ML models perform adequately for magnitude prediction and aftershock probability estimation, deep sequential models like LSTM and CNN-LSTM demonstrate superior performance in temporal forecasting of seismic sequences. The paper concludes by discussing the limitations of current AI-based methods in handling uncertainty. It proposes future directions integrating physics-informed neural networks and multi-sensor fusion for improved reliability. Keywords - Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM).