Coupling machine learning algorithms with spatial analytical techniques for landslide susceptibility modeling is a worth considering issue. So, the current research intend to present the first comprehensive comparison among the performances of ten advanced machine learning techniques (MLTs) including artificial neural networks (ANNs), boosted regression tree (BRT), classification and regression trees (CART), generalized linear model (GLM), generalized additive model (GAM), multivariate adaptive regression splines (MARS), naïve Bayes (NB), quadratic discriminant analysis (QDA), random forest (RF), and support vector machines (SVM) for modeling landslide susceptibility and evaluating the importance of variables in GIS and R open source software. This study was carried out in the Ghaemshahr Region, Iran. The performance of MLTs has been evaluated using the area under ROC curve (AUC-ROC) approach. The results showed that AUC values for ten MLTs vary from 62.4 to 83.7%. It has been found that the RF (AUC = 83.7%) and BRT (AUC = 80.7%) have the best performances comparison to other MLTs.

结合机器学习算法和空间分析技术进行滑坡敏感性建模是一个值得考虑的问题。因此，当前的研究旨在对包括人工神经网络（ANN），增强型回归树（BRT），分类和回归树（CART），广义线性模型在内的十种高级机器学习技术（MLT）的性能进行首次全面比较。 （GLM），广义加性模型（GAM），多元自适应回归样条（MARS），朴素贝叶斯（NB），二次判别分析（QDA），随机森林（RF）和支持向量机（SVM）来对滑坡敏感性和滑坡进行建模评估GIS和R开源软件中变量的重要性。这项研究是在伊朗Ghaemshahr地区进行的。已使用ROC曲线下面积（AUC-ROC）方法评估了MLT的性能。结果显示，十个MLT的AUC值从62.4到83.7％不等。已经发现，与其他MLT相比，RF（AUC = 83.7％）和BRT（AUC = 80.7％）具有最佳性能。