水利水电科技进展2025，Vol.45Issue(3)：77-85,9.DOI:10.3880/j.issn.1006-7647.2025.03.011
基于可解释机器学习的混凝土重力坝变形安全监控模型
Deformation safety monitoring model of concrete gravity dam based on interpretable machine learning
摘要
Abstract
In order to solve the problem that current machine learning-based dam safety monitoring models cannot give the explanation of the model prediction,the Shapley additive explanations(SHAP)value theory was introduced,and combined with the light gradient boosting machine(LightGBM)model,an interpretable safety monitoring model for concrete gravity dam deformation was established.The model can quantify the specific contribution of each impact factor.The verification results of an engineering example show that the model considers the complex nonlinear relationship between deformation and the environmental quantities,which is closer to the real situation,and it not only has good fitting accuracy and prediction accuracy,but also can interpret the model globally and locally.关键词
混凝土重力坝/变形安全监控/可解释机器学习/SHAP值理论/LightGBM模型Key words
concrete gravity dam/deformation safety monitoring/interpretable machine learning/SHAP value theory/LightGBM model分类
水利科学引用本文复制引用
程琳,袁喜娜,马春辉,贾冬焱,徐笑颜..基于可解释机器学习的混凝土重力坝变形安全监控模型[J].水利水电科技进展,2025,45(3):77-85,9.基金项目
国家自然科学基金项目(52479133) （52479133）
国家自然科学基金-黄河水科学研究联合基金项目(U2443230) （U2443230）
