Machine learning technique in the north zagros earthquake prediction
Salma Ommi, M. Reza Hashemi
Abstract
Studying the changes in seismicity, and the potential of the occurrences of large earthquakes in a seismic zone is not only extremely important from the aspect of seismological research, but it is additionally significant in the decisions of crisis management. Since, nowadays Machine learning techniques have proven the high ability for analyzing information, and discovering the relations among the parameters, in this research were tested some of these techniques for the earthquake prediction. For analysis, the north Zagros seismic catalogue was selected. A region that is an active seismic zone, and large cities are located there. Moreover, nine seismic parameters were used to study the possibility of large earthquake prediction for 1 month using three different Machine Learning (ML) techniques (Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM)). The accuracy of prediction models was evaluated using four different statistical measures (recall, accuracy, precision, and F1-score). The results showed that the (ANN) method is more accurate than other methods. Based on three investigated methodologies, greater accuracy results have been produced to forecast the earthquakes with bigger scale earthquakes about the completeness of the seismic catalogue in large magnitude. These achievements promise the possibility of successful prediction in a short period, which is hopeful for better crisis management performance.