Dam Deformation Prediction Based on Wavelet Neural Network Considering Multiple Influencing Factors

Yaxing Guo

doi:10.54097/xvpdhc13

Authors

Yaxing Guo

DOI:

https://doi.org/10.54097/xvpdhc13

Keywords:

Impact Factor; Spearman; Wavelet Function; Wavelet Neural Network.

Abstract

In order to predict the deformation displacement of the dam and maintain the safety of the dam operation, this paper takes the Xiaolangdi Dam in Luoyang City, Henan Province as the research object, and uses the 52-period monitoring data of a monitoring point on the dam as the data source. The Spearman rank correlation coefficient analysis method was used to analyze the correlation of 15 influencing factors affecting the dam deformation, and the main influencing factors were determined. The wavelet neural network is constructed, the optimal wavelet function and the number of hidden layer nodes are determined, and the dam displacement prediction is realized. By comparing with BP neural network, the superiority of wavelet neural network is verified. The results show that the main factors affecting dam deformation are T0, sinT, cosT and H, respectively. The RMSE and MAE of the prediction results of the wavelet neural network are 0.1932 dm and 0.1398 dm, respectively, which are reduced by 0.6092 dm and 0.444 dm compared with the BP neural network. The research results can provide some reference value for the safe operation and maintenance of the dam.

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