感潮河段的水位变化过程较为复杂,其精确预测、评估对流域防洪减灾及生态安全具有重要意义。针对长江感潮河段水位变化受上游径流与下游潮汐共同作用而呈现出的非线性、动态复杂波动特征,提出了一种融合卷积神经网络(CNN)、长短期记忆(LSTM)网络和科尔莫戈洛夫-阿诺德网络(KAN)的CNN-LSTM-KAN组合预测模型。首先利用CNN有效提取水位序列的空间特征,随后由LSTM捕捉水位变化的动态时间关联性,最后引入KAN增强模型的非线性表达与动态演变表达能力,实现南京水文实验站水位的实时预测。研究结果表明,CNN-LSTM-KAN组合模型在南京水文实验站实测数据基础上表现了良好的实时预测,在预测精度、峰值捕捉能力和对输入数据的鲁棒性方面均优于优化的LSTM、CNN-LSTM等传统深度学习方法,测试集模型性能评估指标均方根误差(RMSE)为0.065 4 m,平均绝对误差(MAE)为0.042 9 m,平均绝对百分比误差(MAPE)为0.023 5,纳什效率系数(NSE)可达0.995 1。该组合模型结构简单、易于实施,可为长江下游感潮河段的水旱灾害防控及经济社会可持续发展提供有力的技术支撑。

[Objective] Water level variations in tidal river reaches are highly complex and influenced by both upstream runoff and downstream tides. Accurate prediction of these variations is crucial for flood control, disaster reduction and ecological security. Taking the Nanjing Hydrological Experimental Station in the tidal reach of the Yangtze 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 water 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. [Results] The hybrid model shows strong real-time prediction performance on the measured data from the Nanjing station. It outperforms conventional deep learning methods such as optimized LSTM and CNN-LSTM in terms of prediction 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 error (MAPE) of 0.023 5, and a Nash-Sutcliffe efficiency coefficient (NSE) of 0.995 1. [Conclusions] The proposed 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.