Towards Universal Runoff Forecasting: A KAN-WLSTM Framework for Robust Multi-Basin Hydrological Modeling

Accurate river runoff prediction plays a vital role in water resource management, agricultural scheduling, disaster prevention, and climate adaptation. To address three long-standing challenges in multi-basin hydrological modeling—the insufficient nonlinear expressiveness of recurrent structures, underestimation of extreme high-flow events caused by sample imbalance, and weak cross-basin generalization—this study proposes a hybrid forecasting framework, KAN–WLSTM, that integrates physical priors with deep learning. Specifically, (i) the KAN replaces linear layers to achieve nonlinear mapping consistent with hydrological mechanisms; (ii) a WMSE loss is adopted to emphasize high-flow samples; (iii) Granger causality analysis is applied for causality-driven input selection; and (iv) Optuna is used to perform Bayesian-based adaptive hyperparameter optimization. Multi-scale experiments based on the CAMELS-GB dataset show that a 14-day lag window yields the best performance, with an average MSE = 1.77 (m3/s)2 and NSE of 0.81 across nine representative catchments. Comparative results indicate that the proposed model achieves the best or near-best scores in most metrics, outperforming traditional LSTM by 6.8% in MSE and 2.7% in NSE, while reducing peak discharge errors by up to 18%. In large-sample evaluations across 161 catchments, the KAN–WLSTM model attains an average and median NSE of 0.770 and 0.827, respectively, with the smallest variance and ranked first among all models, demonstrating outstanding robustness and generalization under diverse hydro-climatic conditions.