Abstract
This study evaluates the Soviet-era ground motion prediction equation (named as A&K-1979) and introduces an Artificial Neural Network (ANN)-based GMM specifically designed for Azerbaijan to improve prediction accuracy. Ground motion models (GMMs) are essential for predicting earthquake-induced ground motions, aiding seismic hazard assessments and engineering designs. While traditional linear empirical models have been widely used, they often struggle to capture complex nonlinear ground motion behaviors. The performance of A&K-1979 was assessed using a strong-motion dataset comprising 500 records collected between 2022 and 2024. Two variants of A&K-1979 were tested: A&K-1979-1 for PGA ≥160 cm/s2 and A&K-1979-2 for PGA <160 cm/s2. An ANN-based GMM was developed using earthquake magnitude and hypocentral distance as inputs, followed by three hidden layers (32-32-16 neurons) with the Rectified Linear Unit (ReLU) activation function. The model was validated with a separate dataset of 268 records, evaluated using metrics such as bias, standard deviation of residuals (σ), mean absolute error (MAE), root mean squared error (RMSE), and R2. The A&K-1979 model exhibited notable prediction biases: A&K-1979-1 overestimated PGA values, while A&K-1979-2 underestimated them. The ANN-based GMM achieved improved performance metrics, with a bias of -0.0076, σ of 0.5971, MAE of 0.4416, RMSE of 0.5972, and an R2 of 0.4601. The improved accuracy of the ANN-based GMM highlights its potential as a valuable tool for seismic hazard assessments in Azerbaijan. By providing enhanced prediction capabilities, the ANN model demonstrates greater reliability and practical value than A&K-1979, reinforcing the need for updated predictive models in the region and supporting its use in preliminary hazard analysis.