Comparative assessment of machine learning models for daily streamflow prediction in a subtropical monsoon watershed

Accurate streamflow prediction is critical for flood warning and water resources management in subtropical monsoon watersheds, yet optimal model selection remains challenging. This study compared seven machine learning models, including Linear Regression (LR), Gradient Boosting Regressor, Artificial Neural Network (ANN), Random Forest Extra Trees Regressor, XGBoost (XGB), and Long Short-Term Memory (LSTM), for daily streamflow prediction in the Boluo Watershed, South China. Results demonstrated that LSTM achieved superior performance with NSE and KGE of 0.95, followed by ANN and LR. High-flow evaluation revealed that LSTM maintained robust performance under extreme conditions, achieving NSE of 0.86, 0.80, and 0.45 for flows exceeding the 90th, 95th, and 99th percentiles respectively. For flood peaks, LSTM showed the smallest underestimation of 7 to 20%, compared to 30 to 50% for tree-based models. Feature importance analysis revealed upstream flow from Lingxia Station as the dominant predictor (importance of 0.373 for XGB), reflecting watershed memory effects whereby streamflow is predominantly controlled by antecedent hydrological conditions. Residual analysis identified pronounced heteroscedasticity with increasing prediction errors under high-flow conditions. These findings demonstrate that temporal memory mechanisms provide substantial advantages for streamflow prediction under extreme conditions, offering guidance for model selection in operational flood forecasting systems.