SMamba-KAN: an advanced temporal-nonlinear model for precise water level prediction

Introduction River water levels are influenced by a combination of meteorological and environmental factors. In recent years, with the widespread adoption of Transformer architectures in time series modeling, numerous structural variants have emerged, including Mamba based on structured state space models (SSM) and iTransformer, which employs a Variate Token Embedding strategy. Meanwhile, traditional multilayer perceptron (MLP) structures are increasingly being replaced by Kolmogorov–Arnold Networks (KAN) to enhance non-linear modeling capabilities. Methods Building upon the SMamba variant of Mamba, this study introduces a KAN module to construct a hybrid model named SMamba-KAN. The model is applied to multivariate hydrological and meteorological data from several stations in the Yangtze River Basin to forecast water levels at the Datong hydrological station over the next 15 days. Results Experimental results demonstrate that the proposed model achieves excellent performance across multiple evaluation metrics, with MSE, RMSE, MAE, and MAPE reaching 0.013, 0.117, 0.099, and 2.095%, respectively. Quantitative performance analysis demonstrates that SMamba-KAN exhibits substantial error reduction compared with the original Mamba, decreasing prediction errors by over 90% in MSE. Discussion Relative to its direct baseline SMamba, the incorporation of the KAN module facilitates a significant enhancement in predictive accuracy, further lowering MSE by 54% while maintaining consistent performance stability in MAPE metrics. These results verify the model's high accuracy and strong generalization ability in multivariate water level prediction tasks.