An Adaptive Selection Strategy for Runoff Prediction Models Based on Reinforcement Learning

This study proposes an adaptive selection method for hydrological runoff prediction models based on reinforcement learning(RL), specifically using the deep Q learning(DQN) algorithm. This method adaptively selects the optimal data-driven model, including Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Unit networks (GRU), to optimize prediction performance. Furthermore, the effectiveness of this method is validated through simulation examples. Compared to single models and traditional combined models, this strategy adaptively selects a combination of prediction models based on the characteristics of the prediction target. Consequently, it enhances the dynamic adaptability of model selection, thereby improving the accuracy and stability of predictions.