In order to improve the prediction accuracy of dam deformation,a prediction model based on kernel principal compo-nent analysis(KPCA),global search strategy whale optimization algorithm(GSWOA)and gated recurrent unit(GRU)was con-structed to solve the multicollinearity problem among influence factors of deformation data.Firstly,KPCA was used to reduce the dimension of high-dimensional deformation sequence,and then GSWOA was used to optimize the GRU parameters,so the opti-mal deformation prediction model was constructed.Taking the deformation data of Xiaowan super high arch dam as an example,the prediction effect of KPCA-GSWOA-GRU model was compared with KPCA-WOA-GRU model,PCA-GSWOA-GRU model and traditional models.The results showed that the KPCA-GSWOA-GRU model not only effectively reduced the multi-collinearity problem,but also outperformed the compared model in terms of root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and coefficient of determination(R2).The research results provide a theoreti-cal basis and technical support for verifying the validity of KPCA-GSWOA-GRU model on a wider data set and its application in other dam deformation prediction in the future.
super high arch dam/deformation monitoring/dimension reduction analysis/kernel principal component analysis(KPCA)/global search strategy whale optimization algorithm(GSWOA)/gated recurrent unit(GRU)/Xiaow-an Hydropower Station
维普
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