An Interpretable Machine Learning Model for Cavity Water Depth Based on LightGBM and SHAP
Traditional machine learning models mainly focus on improving the model's prediction accuracy,thus ignoring the interpretability of the prediction results.In this study,a black box model based on LightGBM(Light Gradient Boosting Machine)was established to predict the depth of water accumulation in the cavity of aeration facilities.Compared with commonly used machine learning models such as RF(Random Forest),SVM(Support Vector Machine),and XGBoost(Extreme Gradient Boosting),LightGBM has higher prediction accuracy.The four hyper-parameters of LightGBM are optimized by Bayesian optimization technology,which greatly improves the R2(determinant coefficient)score of the model.The SHAP(Shapley Additive Explanation)post-interpretation method is applied to explain the prediction results of LightGBM globally and locally.The global interpretation indicates that the flow velocity,the impact angle of the water tongue,the height of the ridge,and the flow rate are the main factors affecting the formation of cavity water accumulation.The feature interaction interpretation can be used to explain the complex nonlinear relationship between the features,and the local interpretation can show the influence of each feature of a single sample.The interpretable machine learning model of cavity water depth based on LightGBM-SHAP has a good application prospect in the shape optimization of aeration facilities and the optimization of the model experiment scheme.
interpretable machine learningcavity water accumulationbayesian optimizationLightGBMSHAP
万方数据
维普
