Land subsidence occurrence in the Tasuj plane is becoming more frequent and hazardous in the near future due to the water crisis. To mitigate damage caused by land subsidence events, it is necessary to determine the susceptible or prone areas. This study focuses on producing and comparing land subsidence susceptibility map (LSSM) using boosted regression tree (BRT), random forest (RF), and classification and regression tree (CART) approaches with twelve influencing variables, namely altitude, slope angle, aspect, groundwater level, groundwater level change, land cover, lithology, distance to fault, distance to stream, stream power index, topographic wetness index, and plan curvature. Moreover, by implementing the Relief-F feature selection method, the most important variables in LSSM procedure were identified. The performance of the adopted methods was assessed using the area under the receiver operating characteristics curve (AUROC) and statistical evaluation indexes. The results showed that all the employed methods performed well; in particular, the BRT model (AUROC = 0.819) yielded higher prediction accuracy than RF (AUROC = 0.798) and CART (AUROC = 0.764). Findings of this study can assist in characterizing and mitigating the related hazard of land subsidence events.

中文翻译：

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使用增强回归树，随机森林和分类回归树方法对伊朗塔苏伊飞机地面沉降敏感性图进行比较研究

由于水危机，在不久的将来，Tasuj飞机上的地面沉降会变得越来越频繁且危险更大。为了减轻地面沉降事件造成的破坏，有必要确定易受影响或容易发生的区域。这项研究的重点是使用增强回归树（BRT），随机森林（RF）以及分类和回归树（CART）方法生成并比较具有12个影响变量的土地沉降敏感性图（LSSM），这些影响变量包括海拔高度，坡度，坡度，地下水位，地下水位变化，土地覆盖，岩性，到断层的距离，到溪流的距离，溪流功率指数，地形湿度指数和平面曲率。此外，通过实施Relief-F特征选择方法，可以确定LSSM过程中最重要的变量。使用接收器工作特性曲线（AUROC）下的面积和统计评估指标评估所采用方法的性能。结果表明，所采用的所有方法均表现良好。特别是，BRT模型（AUROC = 0.819）的预测准确度高于RF（AUROC = 0.798）和CART（AUROC = 0.764）。这项研究的结果可以帮助表征和减轻地面沉降事件的相关危害。