Runoff forecasting in Beiliu River based on SAO-optimized LSTM model

Abstract

[Objective] Due to the frequent occurrence of disasters triggered by extreme runoff events under the influence of climate change and human activities, Long Short-Term Memory(LSTM)—a deep learning model—has been widely applied for runoff forecasting. However, it still requires improvements in both accuracy and interpretability. [Methods] A hybrid model for runoff forecasting was developed by combining the Snow Ablation Optimizer(SAO) with the LSTM model. Hydrometeorological characteristics of the watershed(runoff, precipitation, temperature) and six large-scale climate factors selected based on Pearson Correlation Coefficient(PCC) were used as model inputs. The model was compared with the Back Propagation(BP) neural network, and the Shapley value method was applied to analyze the importance and contribution of the input features. [Results] The SAO-LSTM model achieved a Mean Absolute Percentage Error(MAPE) of 0.26, a Coefficient of Determination(R²) of 0.80, and a Nash-Sutcliffe Efficiency(NSE) of 0.80, significantly outperforming both the LSTM and BP models and demonstrating excellent generalization ability. Shapley interpretation result indicated that precipitation was the key driving factor, while large-scale climate factors had no significant impact on small watersheds and failed to improve the model's forecasting performance. [Conclusion] The SAO-LSTM model significantly improves forecasting performance and exhibits excellent generalization ability and robustness. For runoff forecasting in small watersheds, precipitation is the key driving factor, with significantly higher importance than other feature variables, while large-scale climate factors contribute relatively little. The proposed SAO-LSTM model offers higher forecasting accuracy, provides insights into key factors influencing runoff, demonstrates excellent generalization ability, and shows promising application potential, thereby offering model support for flood control and drought decision-making.

Keywords

LSTM / SAO / BP / deep learning / runoff forecasting / Beiliu River / large-scale climate factors / influencing factors