Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall during the last 42 years (1977–2019) as well as 12 conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were 0.968, 0.845, 0.828, and 0.765 for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, 32% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.

群众运动是山区最危险的自然灾害之一。本研究基于864个大规模运动的综合数据集，采用了机器学习（ML）模型进行伊朗的大规模运动敏感性测绘（MMSM），包括过去42年（1977-2019年）的泥石流，滑坡和崩塌。作为12个条件因素。验证阶段的结果表明，RF（随机森林）是大规模运动敏感性图的最可行模型。此外，MARS（多元自适应回归样条），MDA（混合判别加法）和BRT（增强回归树）模型也提供了相对准确的结果。RF，MARS，MDA和BRT的AUC验证生成图的结果分别为0.968、0.845、0.828和0.765。基于RF模型生成的MMSM，研究区域的32％被确定为高和非常高的敏感性等级。多数濒临灭绝的群众运动发生在恰哈马哈尔省和巴赫蒂亚里省的西部和中部。此外，我们的发现表明，高程，倾斜角度，距道路的距离以及距断层的距离是大规模运动的关键因素。我们的结果为决策者减轻自然灾害提供了透视图。