Land subsidence is a hazard that results from conditioning factors that cause environmental change and generate social and economic impacts. Some of these factors may increase dissolution of calcareous stones, change groundwater storage, or stem from mining, faulting, and seismic activity. Semnan Plain, Iran is experiencing land subsidence that, along with secondary impacts like surface fissures, is becoming increasingly troublesome. This study modeled land subsidence and created a susceptibility map using multivariate adaptive regression spline (MARS), mixture discriminant analysis (MDA), and boosted regression tree (BRT) machine-learning methods. Analysis of satellite imagery, documents reporting past subsidence, and a contemporary field survey identified 65 sinkholes on Semnan Plain. Twelve conditioning factors were selected for analysis from a review of the scholarly literature, field investigation, and data availability. The three methods were used to model subsidence from a training subset of the known sites. The models were validated with the remaining subset of subsidence locations. Finally, susceptibility maps were used to predict other sites that are highly likely to see subsidence. Receiver operating characteristic curves and the area under the curves (AUC) were applied to assess the accuracies of the maps. AUC values (0.637, 0.783, and 0.712 for the BRT, MARS, and MDA models respectively) showed that MARS generated the most accurate model, MDA generated the second most accurate, and BRT’s was the least accurate model. Subsidence susceptibility maps as produced here can be useful, meaningful, and functional tools for local and regional planners and policy makers involved in land use planning, resource management, and hazard mitigation.

土地沉降是一种危害，它是由引起环境变化并产生社会和经济影响的调节因素引起的。这些因素中的某些因素可能会增加钙质结石的溶解，改变地下水储量，或源于采矿，断层和地震活动。伊朗的Semnan平原正经历着地面沉降，加上诸如地面裂缝等次生冲击，正变得越来越麻烦。这项研究对土地沉降建模，并使用多元自适应回归样条（MARS），混合判别分析（MDA）和增强回归树（BRT）机器学习方法创建了敏感性图。对卫星图像的分析，报告过去沉降的文件以及当代实地调查发现，在Semnan平原上有65个陷井。从学术文献综述，实地调查和数据可用性中选择了十二个条件因子进行分析。这三种方法用于对来自已知站点的训练子集的沉降进行建模。用沉降位置的剩余子集验证了模型。最后，使用磁化率图来预测其他极有可能沉降的地点。接收器工作特性曲线和曲线下面积（AUC）用于评估地图的准确性。AUC值（BRT，MARS和MDA模型的AUC值分别为0.637、0.783和0.712）表明MARS生成了最准确的模型，MDA生成了第二最准确的模型，而BRT则是最不准确的模型。此处生成的沉陷敏感性图可能是有用，有意义的，