水力发电2026，Vol.52Issue(9)：38-44,7.
基于物理信息的堰塞坝峰值流量预测模型
Peak Flow Prediction Model for Landslide Dams Based on Physical Information
摘要
Abstract
To address the issues of overfitting in small-sample peak flow prediction of natural landslide dam failures and the lack of physical mechanisms in purely data-driven models,a grey-box prediction model integrating fluid dynamics priors and heterogeneous ensemble learning is constructed.The study selects 52 natural landslide failure cases,extracting dimensionless features such as volume,energy and Froude number,and injecting the predicted values from classical empirical formulas as prior variables into the feature space as the prediction benchmark.A pipelined architecture is used for feature scaling and model training,combined with Leave-One-Out Cross-Validation(LOOCV)to thoroughly prevent data leakage.Finally,a stacking ensemble model with log residual space is constructed by fusing random forest,gradient boosting tree and support vector regression,using Bayesian ridge regression as the meta-learner.Independent testing results show that the model has a log space coefficient of determination of 0.894,effectively alleviating the black-box limitations under extremely small sample sizes,and providing a physically interpretable and highly accurate decision support tool for emergency early warning of landslide dam disasters.关键词
堰塞坝/溃决峰值流量/物理先验/集成学习/留一法交叉验证Key words
landslide dam/outburst peak discharge/physical prior/ensemble learning/Leave-One-Out Cross-Validation分类
建筑与水利引用本文复制引用
王秋生,王浩,张凯,周兴波,杜效鹄..基于物理信息的堰塞坝峰值流量预测模型[J].水力发电,2026,52(9):38-44,7.基金项目
国家自然科学基金资助项目(52439007) （52439007）
