Deep learning prediction model for arch dam deformation by incorporating feature factor screening

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.

Keywords

concrete arch dam deformation / harris hawks algorithm / variational mode decomposition / random forest algorithm / long-short-term memory neural network / hydraulic engineering / deformation