Affiliation:
1. 1 State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
Abstract
Abstract
In contrast to the traditional black box machine learning model, the white box model can achieve higher prediction accuracy and accurately evaluate and explain the prediction results. Cavity water depth and cavity length of aeration facilities are predicted in this research based on Extreme Gradient Boosting (XGBoost) and a Bayesian optimization technique. The Shapley Additive Explanation (SHAP) method is then utilized to explain the prediction results. This study demonstrates how SHAP may order all features and feature interaction terms in accordance with the significance of the input features. The XGBoost–SHAP white box model can reasonably explain the prediction results of XGBoost both globally and locally and can achieve prediction accuracy comparable to the black box model. The cavity water depth and cavity length white box model developed in this study have a promising future application in the shape optimization of aeration facilities and the improvement of model experiments.
Funder
National Natural Science Foundation of China
Natural Science Basic Research Program of Shaanxi Province
Natural Science Foundation of Shaanxi Provincial Department of Education
Subject
Atmospheric Science,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering,Water Science and Technology
Cited by
8 articles.
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