中国山区多、地形复杂,构造发育、地质灾害隐患分布广泛.滑坡作为山区最具灾难性的地质灾害之一,严重威胁着人民群众的生命及财产安全.构建滑坡易发性模型能够量化滑坡发生的可能性,对制定防灾措施、减少潜在风险具有重要作用.由于经验驱动模型难以量化,且往往依赖主观判断,近年来,滑坡易发性模型的精度与准确度在从经验驱动和统计理论模型向新兴机器学习方向发展的过程中得到提升.对目前滑坡易发性评价常用的机器学习模型进行综合评述,并针对三峡库区的案例研究,对不同的机器学习技术进行广泛分析和比较.机器学习模型通过结合实地调查资料和历史数据,可绘制滑坡易发性地图,辅助制定滑坡减缓策略.根据滑坡易发性预测模型的准确性和效率,评价几种常用算法的优势和局限性.结果 表明,与一些常用的滑坡易发性制图方法相比,基于树结构的集成算法模型性能更好.此外,高质量的数据库十分重要,深度学习算法的更多应用还有待进一步研究探索.“,”There are many mountainous areas in China,with complex terrain,weak planes and geological structures and wide distribution of geohazards.Landslides are one of the most catastrophic natural hazards occurring in mountainous areas,leading to economic loss and casualties.Landslide susceptibility models are capable of quantifying the possibility of where landslides are prone to occur,which plays a significant role in formulating disaster prevention measures and mitigating future potential risk.Since expert-based models are difficult to quantify and generally depend on the subjective judgments,the accuracy and precision of landslide susceptibility models are now evolving from expert models and statistical learning toward the promising use of machine learning methods.This study presented critical reviews on current machine learning models for landslide susceptibility investigation,an extensive analysis and comparison between different machine learning techniques (MLTs) from ease studies in the Three Gorges Reservoir area was presented.In combination with field survey information as well as historical data,machine learning models were used to map landslide susceptibility and help formulate landslide mitigation strategies.The advantages and limitations of several frequently employed algorithms were evaluated based on the accuracy and efficiency of landslide susceptibility forecasting models.As the result shows,the tree-based ensemble algorithms models achieved better compared with other commonly methods of papping landslide susceptibility.Furthermore,the effect of database quality and quantity is significant,and more applications of some advanced methods (i.e.,deep learning algorithms) are yet to be further explored in further researches.