Machine Learning Prediction of River Freeze-Up Dates Under Human Interventions: Insights from the Ningxia–Inner Mongolia Reach of the Yellow River

Lu Zhang, Suyu Liu, Minhao Fan, Dongling Chen, Ze Yuan, Xiuwei Zhang

The Ningxia–Inner Mongolia reach of the Yellow River (NIMRYR) is among the regions in China most severely affected by ice-related disasters. Yet, no systematic machine learning framework has been established to predict freeze-up dates while accounting for human interventions. Using 1960–2024 observations, this study develops a flexible framework that explicitly considers stage-specific human impacts. Four models—multiple linear regression, support vector regression, extreme gradient boosting, and multilayer perceptron—were evaluated with leave-one-out cross-validation. Selecting predictor identification methods individually for each model and optimizing the number of inputs improved accuracy by 7.6–23%, while hyperparameter tuning added 4.5–46%. Redefining stage-specific thresholds of this predictor to reflect reservoir operation improved accuracy by 10–22%. In contrast, excluding early records (1960–1986) with weaker human activity, a common practice in earlier studies, showed little benefit. During 2021–2024, optimal prediction errors were 0.16, −0.99, −7.61, and 0.07 d, with larger deviations in 2023 linked to abnormal warming and intensified reservoir regulation. XGBoost performed best (MAE = 2.95 d). This study provides a scientific basis for freeze-up prediction in the Yellow River basin and advances understanding of freeze-up mechanisms in seasonally ice-covered rivers.