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