Multilayer perceptron integrated with Kolmogorov–Arnold networks for predicting the water level in the Gezhouba Sanjiang downstream approach channel

ABSTRACT The water level in the downstream approach channel (DAC) of the multi-line ship lock exhibits intricately nonlinear fluctuations. This research integrated Kolmogorov–Arnold networks (KANs), convolutional neural networks (CNNs), external attention (EA), and time-varying filter empirical mode decomposition (TVFEMD) with long short-term memory (LSTM) or gate recurrent unit (GRU) to enhance prediction performance. Compared to the GRU, mean absolute error (MAE) of TVFEMD–EA–CNN–GRU–KAN decreased by 46% to 0.131 m, root mean square deviation (RMSD) by 46% to 0.153 m, mean absolute percentage error (MAPE) by 45 to 0.322%, combined accuracy (CA) index by 49% to 0.103, and coefficient of determination (R2) increased by 7% to 0.971. Compared to LSTM, MAE of TVFEMD–EA–CNN–LSTM–KAN decreased by 52% to 0.140 m, RMSD by 51% to 0.164 m, MAPE by 52 to 0.345%, CA by 55% to 0.111, and R2 increased by 11% to 0.968. A novel contribution was considering the influence of outflow changes caused by hydraulic project regulations in water level prediction, which was rarely addressed in existing studies. By collecting outflow data as one of the input features, the prediction accuracy of hybrid models was enhanced substantially. For TVFEMD–EA–CNN–GRU–KAN and TVFEMD–EA–CNN–LSTM–KAN, including outflow among the input features decreases CA by 20 and 22%, respectively.