Comparative Performance Leadership Table (Gals).

This study focuses on simulating ground motion for the Shamkir Water Reservoir area in Azerbaijan using machine learning algorithms to enhance regional seismic hazard assessments. Given the reservoir’s location in a seismically active zone, potential earthquake-induced impacts on dam infrastructure pose critical safety concerns. A synthetic ground motion database comprising 3,013 records was developed using SeismoArtif software, utilizing earthquake magnitude, hypocentral distance, average shear-wave velocity (VS30) and site class as primary features. Four supervised machine learning models—Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)—were developed to predict Peak Ground Acceleration (PGA). A dual-layered performance evaluation was conducted, comparing the models against each other and against the traditional A&K-1979 Ground Motion Prediction Equation (GMPE). Results demonstrate that while all machine learning models are highly applicable and physically consistent, the traditional GMPE fails significantly, yielding R2 of −6.87 and MAE of 284.88 Gals. Within the machine learning cohort, the Random Forest model achieved the highest training scores (R2: 0.8598), yet the Artificial Neural Network (ANN) emerged as the optimal architecture due to its superior generalization and stability. The ANN led the decisive testing phase with an R2 of 0.8437, an RMSE of 46.00 Gals, and the lowest systematic bias (−1.76 Gals) across all subsets. These findings underscore the robustness of data-driven approaches over fixed-coefficient empirical relationships, specifically highlighting the ANN’s capability to provide unbiased, high-fidelity ground motion estimations for critical infrastructure risk-informed decision-making in Azerbaijan.