摘要
Abstract
A dam deformation prediction model based on KPCA-MPA-LSTM algorithm is proposed to address the issues of multiple influencing factors,strong non-linear relationships in deformation monitoring data,and low accuracy of traditional prediction models.Using kernel principal component analysis(KPCA)to reduce the input parameters of the dam prediction model and optimize the input samples of the prediction model.Using the Marine Predator Algorithm(MPA)to optimize the hyperparameters of the Long Short Term Memory Network(LSTM)and minimize its network error.The results show that the algorithm proposed in this paper has higher accuracy in deformation prediction of Fengman Dam compared to the three comparison algorithms,and has certain significance in dam deformation prediction.关键词
大坝/核主成分分析/海洋捕食者算法/长短期记忆网络/预测Key words
dam/Kernel principal component analysis/Marine predator algorithm/Long short-term memory network/forecast分类
水利科学
