Regional Analysis of Earthquakes and Earthquake Magnitude Estimation with Machine Learning Techniques

Natural disasters, which have been increasing in recent years due to the impact of climate change, pose a significant threat worldwide. Natural disasters, which can cause a large number of human losses and material damages due to their uncertain nature and sudden effects, vary depending on the location and natural environment of the countries. Türkiye located in the Alpine-Himalayan Earthquake Zone, is one of the countries most exposed to earthquake disasters. Although timely prediction of earthquakes is of vital importance in minimizing the destructive effects that may occur during the disaster and increasing resistance to the destructive effects of the disaster, it cannot yet be predicted successfully due to its non-linear chaotic behavior. However, many researchers continue to work on the subject, and earthquake prediction models are actively used in some countries where earthquake disasters occur frequently and cause great destruction. In this study, the magnitudes of future earthquakes were predicted using various machine learning models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Random Forests (RF), Gradient Boosting Algorithm (GB), Extreme Gradient Boosting Algorithm (XGBoost), 2-hidden-layer Artificial Neural Networks (ANN), and an ANN-KNN hybrid learning model. The performances of the established models were evaluated with MSE, MAE, RMSE, and R² metrics; and the ANN-KNN model showed that it was more effective than other models by exhibiting the highest performance with 0.0418 MSE, 0.0030 MAE, 0.0552 RMSE, and 0.7138 R² values. Additionally, unlike other studies, seven regions of Türkiye were considered separately and earthquakes were analyzed in detail according to their geography. The analysis results aim to add a new perspective to the literature.