Machine Learning Applications for Earthquake Magnitude Prediction in Western Turkey

Earthquakes are unpreventable natural disasters that result in many casualties and economic losses in the regions where they occur. Earthquake prediction and seismic risk assessments are essential in minimising these losses. Due to the complex nature of seismic events, it is necessary to use a cutting-edge methodology to predict earthquake occurrence effectively. Machine learning methods have been among the most efficient and current methods for solving complex nonlinear problems and analysing big datasets. Because of this feature, they are widely used for predicting earthquakes and earthquake parameters. This study focuses on applying machine learning methods to analyse seismic events in Western Turkey from 1975 to 2024. The aim is to compare the effectiveness of five machine learning approaches for predicting earthquake magnitudes: Long Short-Term Memory (LSTM), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Decision Tree (DT), Random Forest (RF), and Convolutional Neural Network (CNN). The outcomes of these applied methods are encouraging in terms of the prediction of magnitude. Among all the results, the LSTM method is slightly more successful than the other methods, with a Root Mean Square Error (RMSE) of 0.1391, Mean Square Error (MSE) of 0.0193, Mean Absolute Error (MAE) of 0.1046 and Mean Absolute Percentage Error (MAPE) of 3.0631%, respectively.