%0 Journal Article
%A DENG Si-yuan
%A ZHOU Lan-ting
%A WANG Fei
%A LIU Zhi-kun
%T XGBoost-LSTM Combinatorial Model with Variable Weight for Dam Deformation Prediction and Its Application
%D 
%R 10.11988/ckyyb.20210641
%J Journal of Yangtze River Scientific Research Institute
%P 72-79
%V 39
%N 10
%X A XGBoost-LSTM combinatorial model with variable weight is proposed to more accurately predict dam deformation. First,the XGBoost (eXtreme Gradient Boosting) model and LSTM (Long Short-Term Memory) model are introduced to analyze and predict the dam deformation respectively,and then the results of the two models are combined by using variable weight combination method to obtain the final prediction result. With a concrete gravity dam as a case study,the advantages of XGBoost and LSTM models in dam deformation prediction are demonstrated respectively through comparison with those of random forest,ELMAN and stepwise regression analysis models;furthermore,the prediction effect of the combinatorial model is verified to have enhanced remarkably compared with each of the single model and the equivalent-weighted XGBoost-LSTM combinatarial model. The deformation prediction results are more consistent with the actual engineering situation,thus is well applicable and popularizable.
%U http://ckyyb.crsri.cn/EN/10.11988/ckyyb.20210641