水力发电2024，Vol.50Issue(5)：45-53,9.
基于PCA-SSA-XGBoost算法的拱坝应力预测模型研究
Research on Arch Dam Stress Prediction Model Based on PCA-SSA-XGBoost Algorithm
摘要
Abstract
Because the dam stress is affected by many factors such as water level and temperature,the interrelationship between these influencing factors will cause multiple collinear problems,which may easily lead to the pseudo-regression phenomenon of the prediction model using this as the input.In addition,the existing stress prediction models based on machine learning algorithms are prone to overfitting due to too many training features and overtraining,and their prediction accuracy needs to be improved.In response to above problems,an improved limit gradient lifting algorithm(PCA-SSA-XGBoost)based on principal component analysis(PCA)and sparrow search algorithm(SSA)is proposed to construct an arch dam stress prediction model.The model first uses principal component analysis to reduce the dimensions of the parameters to reduce the impact of multicollinearity of the influencing factors,and then,the hyperparameters of XGBoost are optimized through the SSA algorithm to avoid the overfitting of traditional algorithm and further improve the prediction performance of model.The model is applied to a concrete arch dam project in southwest China to process,analyze and predict stress and stress-related monitoring data,and the prediction result is combined with that of multiple linear regression models(MVLR),neural network models(RBFNN)and limit gradient lifting regression prediction models(XGBR).The comparisons show that the stress prediction model based on the PCA-SSA-XGBoost algorithm can overcome the multicollinearity and overfitting problems of input variables,and has superiority in prediction accuracy.关键词
拱坝/应力预测/主成分分析/极限梯度提升/麻雀搜索Key words
arch dam/stress prediction/PCA/XGBoost/SSA分类
建筑与水利引用本文复制引用
崔博,安惠伦,陈文龙,王佳俊..基于PCA-SSA-XGBoost算法的拱坝应力预测模型研究[J].水力发电,2024,50(5):45-53,9.基金项目
国家重点研发计划(2018YFC0407101) （2018YFC0407101）
国家自然科学基金资助项目(51909187 （）
51879186) （）
