# Explainable Machine-Learning Predictions for Peak Ground Acceleration
###### Abstract
However, these machine learning models established by the existing research mostly use magnitude, epicentral distance, etc., as input parameters, and make less use of parameters that characterize site characteristics. The influence of site conditions on seismic activity cannot be ignored, so it is necessary to consider site conditions in input features. For site effects, [PERSON] et al. [26] developed an amplification model using the random forest algorithm, which achieved better performance than the physics-based modeling (GRA) using detailed 1D velocity profiles. [PERSON] et al. [27] used machine learning to establish ground motion prediction maps and provided a ground motion variability consistent with advanced numerical simulations results based on detailed subsoil models. It is equally important to understand the reason why the model makes a prediction as well as the accuracy of the prediction. Simple models (such as linear models, single decision tree models, etc.) are interpretable. Ensemble learning models or deep learning models have better prediction performance and higher model complexity, which reduces the interpretability of the model. At present, some methods have been applied to the explanation of these models, such as LIME (Local Interpretable Model Agnostic Explanation) [28] and SHAP (SHapley Additive exPlanations) [29; 30; 31]. LIME is a local explanation method that can only be used to evaluate the contributions of features to a single prediction and cannot be used to explain the model globally. SHAP does not have this limitation. It can not only explain a single sample locally, but it can also explain all samples globally. Due to its unique advantages, the SHAP method has been successfully applied to many scientific fields such as biomedical engineering [32; 33; 34], ecological engineering [35], concrete strength prediction [36; 37], and the prediction of rheological properties of self-compacting concrete [38]. It has also been applied in earthquake engineering: for example, [PERSON] et al. [39] established an explainable prediction model of ground motion parameters based on SHAP. Based on the ground motion data of the KiK-net strong motion network in Japan, the feature combination with the best performance of the models were selected by feature importance ranking, and the PGA prediction models based on ground motion parameters and site characteristic parameters were established by XGBoost (eXtreme Gradient Boosting). The SHAP was used to explain the machine learning models, and the influence of each feature on the prediction results was analyzed. Based on the ground motion data of the KiK-net strong motion network in Japan, this paper established the machine learning model for predicting PGA. The input parameters included both ground motion characteristics and site characteristics, and SHAP was used to explain the model. Firstly, we introduced the data and algorithm principles required for machine learning model training. Then, two ground motion characteristics and nine site characteristics were selected for feature selection, and 14 feature importance ranking methods were used for comprehensive scoring. We finally selected the feature combinations that made the model perform best, including 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 \(V_{\rm s}\)), as the input features for machine learning. Additionally, we compared the prediction results with the measured records and numerical simulation results, respectively. Finally, the SHAP method was used to explain the machine learning model and the variation laws of the SHAP value of each input feature were analyzed.