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

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.