Prediction of Sluice Seepage Based on Impact Factor Screening and the IKOA-BiGRU Model

Sluices play a critical role in flood control, power generation, water supply, etc. With decades of service, sluice safety assurance becomes a structural engineering imperative. Previous investigations have revealed that failures of sluices are often associated with seepage damage. To gain further insight into sluice seepage and ensure the safety of sluice structures, proposing an effective prediction method for sluice seepage nevertheless remains a challenging fundamental and practical perspective. Therefore, in this paper, a novel prediction model for sluice seepage based on impact factor screening methods, the improved Kepler optimization algorithm (IKOA) and the bidirectional gated recurrent unit (BiGRU), is presented. Primarily, the maximal information coefficient and the correlation-based feature selection (MIC–CFS) are introduced to screen the impact factors of the model, aiming to reduce redundant information and the complexity of the model. Subsequently, the Kepler optimization algorithm (KOA) is enhanced using three strategies: chaotic mapping-based initialization, Runge–Kutta-based position updating, and the enhanced solution quality (ESQ) strategy to optimize the hyperparameters of the BiGRU network. On this basis, the prediction model is established, which is applied in the Bengbu sluice to verify its fitting and prediction performance. Eventually, comparison analyses with a traditional stepwise regression model, IKOA–LSTM, and IKOA–GRU, were conducted based on monitoring sequences of three monitoring points. The coefficients of determination of the proposed model were located in the range of 0.974 to 0.988. Correspondingly, the mean absolute error values of the proposed model were the lowest, ranging from 0.074 to 0.064. The results of six evaluation metrics confirm that the proposed model consistently exhibits superior interpretability and is able to serve as a promising tool for sluice seepage prediction.