Enhancing Daily Streamflow Prediction: A Hybrid Deep Learning Framework Integrating Temporal Convolutions, Kolmogorov–Arnold Networks, and Multihead Attention

Abstract Accurate and reliable daily streamflow prediction is essential for effective water resources management, reservoir operation, drought mitigation, and early flood warning, yet it remains a persistent challenge due to the highly nonlinear, multiscale, and nonstationary characteristics of hydrological systems. To address these complexities, this study proposes DTCN-KAN-MA, a novel hybrid deep learning framework that synergistically integrates three key components. Dynamic dilated temporal convolutional networks (DTCNs) hierarchically extract short- to long-term temporal patterns across multiple scales. Kolmogorov–Arnold networks (KANs), replace traditional fixed activation functions with learnable spline-based functions to explicitly capture intricate nonlinear rainfall-runoff transformation. A multihead attention (MA) mechanism adaptively emphasizes the most informative hydrometeorological variables at each time step. The model was evaluated using comprehensive, basin-specific data sets from two hydroclimatically contrasting regions—the semi-arid Lanzhou station on the Yellow River and the humid Hekou station on the Diaojiang River. Results showed that DTCN-KAN-MA achieved Nash–Sutcliffe efficiency (NSE) values of 0.98 at both sites, comparable to the best individual benchmark (TCN), with a difference of only 1–2 percentage points. While the improvement in variance explanation was marginal, the combined model demonstrated lower error metrics, yielding RMSE–observations standard deviation ratio (RSR) scores of 0.14 and 0.12. Thus, the hybrid approach offers refined accuracy over standalone methods rather than a substantial gain in overall fit. Robustness analyses under input noise and ablation experiments further confirmed the model’s stability and the pivotal role of the KAN module in boosting generalization. The results indicated that the DTCN-KAN-MA model combined data-driven forecasting capabilities with improved predictive accuracy in the two studied basins, suggesting its potential utility for operational streamflow prediction in similar climatic settings. The model generated accurate daily streamflow forecasts for the two studied basins, offering a viable tool for informing flood warning, reservoir operation, and irrigation management.