Dam Deformation Prediction Method Based on HHO-QRNN Model
In order to effectively utilize the real information in the dam displacement dataset,improve the accuracy of the prediction model,and reduce the training time required for modeling and analysis,this paper proposes a dam displace-ment prediction method based on Kalman filter algorithm,complete ensemble empirical mode decomposition with adap-tive noise and quasi-recursive neural network.Firstly,the model uses Kalman filtering algorithm to process the original input data for effective information extraction to eliminate the influence of observation noise.Secondly,a signal decompo-sition algorithm is designed to extract trend term,periodic term and random term data sets from cumulative displacement values to separate the influence of different inducing factors on the amount of dam displacement.Finally,a displacement prediction algorithm based on the improved Harris Hawk algorithm optimized quasi-recursive neural network is proposed,and the prediction results are superimposed on different data sets to obtain the final prediction results.Taking the histori-cal displacement observation data set of a reservoir dam as an example,the model of this paper is compared with other traditional prediction models.The results show that the prediction accuracy and training speed of this model are signifi-cantly improved,which verifies its feasibility and advancement.
dam deformation predictionHarris Hawk optimization algorithmquasi-recursive neural networkdeep learning
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