# Explainable Machine-Learning Predictions for Peak Ground Acceleration
###### Abstract  
Peak ground acceleration (PGA) prediction is of great significance in the seismic design of engineering structures. Machine learning is a new method to predict PGA and does have some advantages. To establish explainable prediction models of PGA, 3104 sets of uphole and downhole seismic records collected by the KiK-net in Japan were used. The feature combinations that make the models perform best were selected through feature selection. The peak bedrock acceleration (PBA), the predominant frequency (FP), the depth of the soil when the shear wave velocity reaches 800 m/s (D800), and the bedrock shear wave velocity (Bedrock \(V_{S}\)) were used as inputs to predict the PGA. The XGBoost (eXtreme Gradient Boosting), random forest, and decision tree models were established, and the prediction results were compared with the numerical simulation results The influence between the input features and the model prediction results were analyzed with the SHAP (SHapley Additive exPlanations) value. The results show that the \(R^{2}\) of the training dataset and testing dataset reach up to 0.945 and 0.915, respectively. On different site classifications and different PGA intervals, the prediction results of the XGBoost model are better than the random forest model and the decision tree model. Even if a non-integrated algorithm (decision tree model) is used, its prediction effect is better than the numerical simulation methods. The SHAP values of the three machine learning models have the same distribution and densities, and the influence of each feature on the prediction results is consistent with the existing empirical data, which shows the rationality of the machine learning models and provides reliable support for the prediction results. machine learning; XGBoost; explainable; SHAP; prediction of PGA +
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For areas with strong motion stations, the PGA can be obtained directly from the stations. In addition, the PGA can be predicted by ground motion prediction equations (GMPEs) and numerical simulation. GMPEs [11; 12; 13; 14] are essentially predictions of ground motion parameters for a certain type of site with common characteristics. They have the characteristics of clear forms and fast calculation speeds, and they are commonly used in ground motion zoning and probabilistic seismic hazard analysis. However, most GMPEs have insufficient consideration of site effects, so when they are used for specific sites, the necessary amendments need to be made according to the site's characteristics. Numerical simulation [15; 16; 17; 18; 19] includes one-dimensional, two-dimensional, and three-dimensional analysis methods. The one-dimensional method has a simple constitutive relationship and can consider the relative high-frequency components of ground motion at a lower computational cost. Soil seismic response analysis programs based on one-dimensional numerical simulation method, such as SHAKE [20] and DEEPSOIL [21], are widely used in the world, but their calculation results on soft soil sites are not ideal. With the continuous development of artificial intelligence technology, machine learning has also been widely used in the prediction of ground motion parameters. For example, [PERSON] et al. [22] used epicentral distance, focal depth, and magnitude as input parameters to establish a neural network model to predict the PGA of 10 railway stations along Taiwan's high-speed rail system and identify potentially dangerous stations. [PERSON] and [PERSON] [23] established a PGA prediction artificial neural network model suitable for Japan and compared and analyzed the prediction effects of the model with six input features and three input features. [PERSON] et al. [24] compared three artificial neural network methods using the same data and inputs and developed a PGA prediction model for northern Turkey. [PERSON] et al. [25] established a fully data-driven PGA prediction model by avoiding any prior function form specification.