基于自适应时序分解筛选的大坝变形预测模型OA北大核心CSTPCD

High precision analysis and prediction of dam deformation is an important means to master dam work-ing behavior and diagnose dam anomalies.Aiming at the problems such as insufficient information feature mining,weak generalization ability and difficulty in accurate prediction of existing models,grey Wolf algorithm was used to optimize the complete ensemble empirical mode decomposition with adaptive noise to solve the multidimensional parameter calibration problem,and threshold evaluation indexes were used to retain the effective information fea-tures of deformation time series data.The cross-validation method is combined with recursive feature selection method,and the optimal factor subset is selected by multiple learners to remove redundant features,extract effec-tive information and enhance the interpretability of the model.Considering the characteristics of time series data,the number of steps in the time window of the bidirectional long short term memory neural network is optimized,and in order to construct dam deformation analysis and prediction model,several methods such as noise reduction of dam deformation data and input of optimal feature factors are used.The results show that the model has the a-bility of accurately mining nonlinear information,and the prediction performance has been significantly improved,which can provide reference for dam safety monitoring.