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

Main Article Content

Elçin Gök
https://orcid.org/0000-0002-2643-1453
Ilknur Kaftan
https://orcid.org/0000-0002-1861-9894

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.

Article Details

How to Cite
Gök, E. and Kaftan, I. (2022) “Prediction of Peak Ground Acceleration by Artificial Neural Network and Adaptive Neuro-fuzzy Inference System”, Annals of Geophysics, 65(1), p. SE106. doi: 10.4401/ag-8659.
Section
Seismology
Author Biographies

Elçin Gök, Dokuz Eylul University

Geophysics

Ilknur Kaftan, Dokuz Eylul University

Geophysics