# Explainable Machine-Learning Predictions for Peak Ground Acceleration
## 5 Conclusions  
Based on 3104 sets of ground motion records from the KiK-net, XGBoost was used to train the PGA prediction model. Through feature selection, four parameters representing ground motion characteristics (PBA, FP) and site conditions (D800, Bedrock \(V_{\mathrm{s}}\)) were selected as input features of the machine learning model. For comparative analysis, a random forest model and a decision tree model were trained to predict the PGA. In order to explain the machine learning models and analyze whether the prediction results of the machine learning models conform to the objective reality and physical laws, the SHAP was introduced to explain the model and analyze the characteristics. The main conclusions are as follows:  
(1) The \(R^{2}\) of XGBoost in the training set and testing set reach up to 0.945 and 0.915, respectively. In addition, the relative error of about 80% samples is within \(\pm\)30%. The model has good prediction and generalization ability. (2) On different site classification and different PGA intervals, the prediction effect of XGBoost model is better than that of the other two machine learning models. Except for individual working conditions, the prediction results of machine learning models, even if it is a non-integrated algorithm, are better than that of the numerical simulation methods. Especially on strong ground motion (PGA > 0.2 g), the nonlinearity of soil is enhanced, and the calculation errors of numerical simulation methods increase significantly, which reflects the limitation of the linear equivalent method. The increase in the prediction error of the machine learning models is obviously smaller than that of numerical simulation methods. (3) The influence of input features shown by SHAP value on PGA prediction results is consistent with the physical law. The PBA is the most important feature that affects the prediction results of the PGA. The SHAP values of the PBA increase with the increase in the PBA; that is, the prediction value of the PGA increases with the increase in the PBA. D800 is a secondary important feature, when D800 is less than 50 m, the SHAP values are positive, which have a positive impact on the prediction results of the PGA; when D800 is greater than 50 m, the SHAP values are mainly negative, which have a negative impact on the prediction results of the PGA. The SHAP values of Bedrock \(V_{\mathrm{s}}\) and FP are mainly concentrated near 0, which have little effect on the prediction results of the PGA. The machine learning model established in this paper has achieved good prediction performance, and SHAP analysis proves the rationality of the prediction results, which provides a new method for PGA prediction. Although the data come from a Japanese site, it does not mean that the model in this paper is only applicable to Japan, as the site amplification effect has certain universality. However, due to the limited amount of data, it needs to be further verified. Research on this topic can be supplemented and improved in the following aspects:  
(1) Using global seismic datasets, not just Japanese seismic datasets, to verify the model in this paper or establish a new model for PGA prediction;  
(2) The ground motion data used in this article are all from the station records of the horizontal sites, without considering the influence of complex terrain factors, and further research can predict the PGA under different terrain;  
(3) Deep learning algorithms such as neural networks can be used to predict PGA in the further research, and the prediction results can be compared with the XGBoost method to select the best performing model. Figure 16: Force plot of the typical sample for XGBoost. **Author Contributions:** Conceptualization, R.S. and T.Z. ; methodology, W.Q. and J.Q. ; writing--original draft preparation, W.Q. and R.S. ; writing--review and editing, R.S., W.Q., and J.Q. ; funding acquisition, R.S. and T.Z. All authors have read and agreed to the published version of the manuscript.