Machine-Learning-Based Ensemble Prediction of the Snow Water Equivalent in the Upper Yalong River Basin

Jujia Zhang, Mingxiang Yang (), Ningpeng Dong and Yicheng Wang
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Jujia Zhang: Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Mingxiang Yang: Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Ningpeng Dong: Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Yicheng Wang: Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

Sustainability, 2025, vol. 17, issue 9, 1-23

Abstract: The snow water equivalent (SWE) in high-altitude regions is crucial for water resource management and disaster risk reduction, yet accurate predictions remain challenging due to complex snowmelt processes, nonlinear meteorological factors, and time-lag effects. This study used snow remote sensing products from the Advanced Microwave Scanning Radiometer (AMSR) as the predictand for evaluating SWE predictions. It applied nine machine learning models—linear regression (LR), decision trees (DT), support vector regression (SVR), random forest (RF), artificial neural networks (ANNs), AdaBoost, XGBoost, gradient boosting decision trees (GBDT), and CatBoost. For each machine learning model, submodels were constructed to predict the SWE for the next 1 to 30 days. The 30 submodels of each machine learning model formed the prediction model for the snow water equivalent over the next 30 days. Through an accuracy evaluation and ensemble forecasting, the snow water equivalent prediction for the next 30 days in the Yalong River above the Ganzi Basin was finally achieved. The results showed that for all models, the average Nash–Sutcliffe Efficiency (NSE) rate was greater than 0.8, the average root mean square error (RMSE) was under 8 mm, and the average relative error (RE) was below 7% across three lead time periods (1–10, 11–20, and 21–30 days). The ensemble average model, combining ANNs, GBDT, and CatBoost, demonstrated superior accuracy, with NSE values exceeding 0.85 and RMSE values under 6 mm. A sensitivity analysis using the Shapley Additive Explanations (SHAP) model revealed that temperature variables (average, minimum, and maximum temperatures) were the most influential factors, while relative humidity (Rhu) significantly affected the SWE by reducing evaporation. These findings provide insights for improving SWE prediction accuracy and support water resource management in high-altitude regions.

Keywords: snow water equivalent (SWE); machine learning; ensemble mean; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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