Prediction of Peak Ground Acceleration by Artificial Neural Network and Adaptive Neuro-fuzzy Inference System

Abstract

An attenuation relationship model belonging to a region with a high earthquake hazard is important. It is used for engineering studies to know how the peak ground acceleration (PGA) value depends on the distance where there are no stations. This study used earthquakes with magnitudes greater than 4 that IzmirNET recorded between 2009 and 2017 to determine the PGA through an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), which are widely applied in engineering seismology studies. For this purpose, 2925 records from 62 earthquakes were analysed in the ANN and ANFIS applications. Magnitude, focal depth, hypocentral distance (Rhyp), and site conditions comprise the inputs, and PGA values are the outputs. Using the Karaburun earthquake, we compared the ANN and ANFIS models using different ground motion prediction equations (GMPE) and the appropriate criteria. We determined the proximate values to PGA values measured at IzmirNET stations of the Karaburun earthquake, which was M = 6.2 in 2017, were used to test the ANN and ANFIS. The results were examined and indicated that the ANN and ANFIS are good candidates for obtaining PGA values for future earthquakes in the studied area. In addition, the PGA values of subsequent earthquakes can be calculated more quickly without any preliminary evaluation using an ANN and ANFIS.