基于CNN-LSTM-KAN的长江下游感潮河段水位实时预测模型及其应用OA

[Objective]Water level variations in tidal river reaches are highly complex and influenced by both up-stream runoff and downstream tides.Accurate prediction of these variations is crucial for flood control,disaster re-duction and ecological security.Taking the Nanjing Hydrological Experimental Station in the tidal reach of the Yan-gtze River as a case study,this paper aims to develop a model that can capture the nonlinear,dynamic and complex fluctuation characteristics of water levels and achieve real-time prediction.[Methods]A hybrid CNN-LSTM-KAN model is proposed,which combines a Convolutional Neural Network(CNN),a Long Short-Term Memory(LSTM)network,and a Kolmogorov-Arnold Network(KAN).First,the CNN is used to extract spatial features from the wa-ter level sequence.Then,the LSTM captures the dynamic temporal dependencies of water level changes.Finally,KAN is introduced to further enhance the model's ability to represent nonlinear and dynamic characteristics.[Re-sults]The hybrid model shows strong real-time prediction performance on the measured data from the Nanjing sta-tion.It outperforms conventional deep learning methods such as optimized LSTM and CNN-LSTM in terms of predic-tion accuracy,peak capture capability and robustness to input data.On the test set,the model achieves a root mean square error(RMSE)of 0.065 4 m,a mean absolute error(MAE)of 0.042 9 m,a mean absolute percentage er-ror(MAPE)of 0.023 5,and a Nash-Sutcliffe efficiency coefficient(NSE)of 0.995 1.[Conclusions]The pro-posed CNN-LSTM-KAN hybrid model has a simple structure and is easy to implement.It provides strong technical support for flood and drought disaster prevention and sustainable socio-economic development in the tidal reaches of the lower Yangtze River.