水利水电技术（中英文）2025，Vol.56Issue(3)：123-134,12.DOI:10.13928/j.cnki.wrahe.2025.03.010
融合特征因子筛选的拱坝变形深度学习预测模型
Deep learning prediction model for arch dam deformation by incorporating feature factor screening
摘要
Abstract
[Objective]Deformation is a direct characterization of the overall serviceability of dams under the coupling of reservoir water,temperature and material properties,etc.The establishment of an accurate and efficient prediction model is of great significance in grasping the deformation trend of dams and assessing the risk of dams.[Methods]Aiming at the problems of low accuracy,poor adaptability and weak noise immunity of traditional prediction models,a deep learning prediction model for concrete arch dam deformation is proposed by combining the Harris Hawk algorithm(HHO),Variational Modal Decomposition(VMD),Random Forest algorithm(RF),and Long-Short-Term Memory neural network(LSTM).First,the HHO algorithm is improved by introducing Tent chaotic mapping,energy randomness decreasing strategy,and the arch dam deformation data sequence is decomposed to obtain a number of modal components(IMF)with different frequencies using the IHHO-VMD method.Secondly,The RF algorithm is utilized to calculate the contribution of deformed characteristic factor and to screen the optimal set of input factors for the prediction model;.Finally,the LSTM model is used to learn and predict each IMF component,and the final deformation prediction is obtained by reconstructing the predicted values of each component.[Results]The simulated signal decomposition result show that compared with the existing signal decomposition method,the optimal signal decomposition can be realized by using the IHHO-VMD method.Analyzed by a project example,the proposed model predicts the displacement of four measurement points with average RMSE,MAE,R2 and MAPE of 0.397 6 mm,0.327 5 mm,0.991 8 and 1.519 4％.[Conclusion]Compared with other combined models,the result of the four evaluation indexes of the proposed model are optimal,indicating that the model has the advantages of high prediction accuracy,good generalization ability and robustness.关键词
混凝土拱坝变形/哈里斯鹰算法/变分模态分解/随机森林算法/长短时记忆神经网络/水利工程/变形Key words
concrete arch dam deformation/harris hawks algorithm/variational mode decomposition/random forest algorithm/long-short-term memory neural network/hydraulic engineering/deformation分类
水利科学引用本文复制引用
刘桓辰,朱静,郭梦京..融合特征因子筛选的拱坝变形深度学习预测模型[J].水利水电技术（中英文）,2025,56(3):123-134,12.基金项目
国家自然科学基金项目(41807156) （41807156）
陕西省教育厅重点实验室科研计划项目(18JS073) （18JS073）
