Improving Individual and Regional Rainfall–Runoff Modeling in North American Watersheds Through Feature Selection and Hyperparameter Optimization

Precise rainfall-runoff modeling (RRM) is vital for disaster management, resource conservation, and mitigation. Recent deep learning-based methods, such as long short-term memory (LSTM) networks, often struggle with major challenges, including temporal sensitivity, feature selection, generalizability, and hyperparameter tuning. The objective of this study is to develop an accurate and generalizable rainfall–runoff modeling framework that addresses the four aforementioned challenges. We propose a novel RRM framework that integrates transductive LSTM (TLSTM) to capture fine-grained temporal changes, off-policy proximal policy optimization (PPO) combined with Shapley Additive exPlanations (SHAP)-based reward functions for feature selection, an enhanced generative adversarial network (GAN) for online data augmentation, and Bayesian optimization hyperband (BOHB) for efficient hyperparameter tuning. TLSTM uses transductive learning, where samples near the test point are given extra weight, to capture fine-grained temporal shifts. Off-policy PPO contributes to this process by selecting features sensitive to temporal patterns in RRM. Our improved GAN conducts online data augmentation by excluding some gradients, increasing diversity and relevance in synthetic data. Finally, BOHB accelerates hyperparameter tuning by merging Bayesian optimization with the scaling efficiency of Hyperband. We evaluate our model using the Comprehensive Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset under individual and regional scenarios. It achieves Nash–Sutcliffe efficiency (NSE) scores of 0.588 and 0.873, surpassing the baseline scores of 0.548 and 0.830, respectively. The generalizability of our approach was assessed on the hydro-climatic datasets for North America (HYSETS), also yielding improved performance. These improvements indicate more accurate capture of flow dynamics and peak events, supporting a robust and interpretable framework for RRM.