水力发电学报2025，Vol.44Issue(5)：33-43,11.DOI:10.11660/slfdxb.20250504
堰塞坝数据库插补及稳定性评价
Database imputation and stability evaluation for landslide dams
摘要
Abstract
Landslide damming and subsequent flood breaches pose significant threats to the human lives and property of the downstream communities,and quantitative analysis of dam stability is essential for downstream risk assessment and disaster prevention.However,incomplete,imbalanced landslide dam databases severely impact the development of data-driven models.This study develops a stability assessment model for landslide dams that integrates five machine learning algorithms-Support Vector Machine(SVM),Random Forest(RF),eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and K Nearest Neighbor(KNN)-and accounts for geomorphological parameters,hydrological conditions,dam materials,and climate factors.We identify optimal methods for imputation of various factors,then apply three sampling techniques(oversampling,undersampling,and hybrid sampling)to balance the data and mitigate model bias while examining their combined effects on model performance.Five-fold cross-validation results indicate the oversampling combined with LightGBM achieves an average accuracy of 0.84,a bias of 0.24,and a comprehensive evaluation index(CEI)of 1.32,outperforming other algorithms.Validation in typical landslide dam cases shows that our new model outperforms previous models,offering a novel approach to emergency response planning.关键词
堰塞坝/稳定性/数据库插补/模型评价/采样方法Key words
landslide dam/stability/database imputation/model evaluation/sampling method分类
水利科学引用本文复制引用
谭龙金,冯震宇,周家文,杨兴国,廖海梅..堰塞坝数据库插补及稳定性评价[J].水力发电学报,2025,44(5):33-43,11.基金项目
国家自然科学基金区域创新发展联合基金项目(U20A20111) （U20A20111）
国家自然科学基金青年基金项目(42107189) （42107189）
