Dam deformation prediction model based on IHHO-LSTM-KAN

Abstract

[Objective] High-precision deformation prediction during the whole life cycle is a key method to evaluate the service behavior of dams and ensure the safe operation of dams. The current prediction model has problems such as insufficient correlation analysis of data feature, low prediction accuracy of short time series data, neglecting the continuous growth properties of the time series, and easy to fall into the local optimum in model training. [Methods] Therefore, a dam dynamic deformation prediction model is proposed, which utilizes the long short-term memory neural network(LSTM) to capture the long-term and short-term dependence of time series, couples the Kolmogorov-Arnold Networks(KAN) mechanism to improve the fully connected layer structure of the network to enhance the ability to characterize the complex data relationship of long and short time series, and adopts multi-strategy improved Harris Hawks optimization algorithm(IHHO) to explore the optimal combination of hyperparameters, so as to optimize the model structure, solve the gradient problem, accelerate the training convergence and improve the prediction performance. [Results] Examples show that the prediction accuracy and generalization ability of the model for short and long time series are better than other deep learning models, and the convergence speed is superior to other intelligent optimization algorithms, and the improvement effect of KAN mechanism on the short time series prediction is more obvious. [Conclusion] The model has good robustness and applicability, which can provide technical reference for the dynamic safety monitoring of the whole life cycle of dams.

Keywords

dam deformation prediction / short time series / long short-term memory / KAN / improved Harris Hawks optimization algorithm / deformation / influencing factors