## 6 Discussion
We have shown that the SVM algorithm has the highest goodness-of-fit and prediction accuracy of the three machine learning algorithms tested in this study based on both training and validation datasets. This result is consistent with the findings of other landslide researchers [112; 113; 114; 115; 116; 117]. For example, [PERSON] et al. [115], compared the performance of SVM, LR, and ANN for landslide assessment in a catchment in the Dodangeh watershed, Mazandaran province, Iran. They concluded that SVM outperformed the other models, and therefore, it was potentially known as the most powerful algorithm for landslide modeling in their study area. [PERSON] et al. [116] compared the performance of the SVM and LMT models for landslide susceptibility mapping in Kamyaran county, also in Kurdistan province, and confirmed the superiority of the SVM model. SVM has also been successfully used in landslide susceptibility mapping in the Cameron Highlands, Malaysia [19]. In contrast, [PERSON] et al. [15] compared LMT, LR, NBT, ANN, and SVM models for landslide susceptibility mapping in Bjar city, Kurdistan province, and found that LMT had the highest, and SVM the lowest, goodness-of-fit and prediction accuracy. According to the best of our knowledge of the literature on landslide susceptibility mapping, SVM can be successfully used as a benchmark computing machine learning algorithm in new ensemble models [118; 119]. For example, [PERSON] et al. [119] proposed a new ensemble model consisting of random subspace base classification and regression tree (RSSCART) for landslide modeling and assessment in the Luc Yen district of Yen Bai province, Viet Nam. They compared their new ensemble model, the SVM benchmark model. SVM offers several advantages over other machine learning models: (i) It is free from feature selection techniques that are required by other models such as decision trees; (ii) it can handle complex and non-linear problems with large datasets; and (iii) it solves the convex quadratic programming optimization problem of separating the hyper-plane and thus is a suitable replacement for artificial neural networks [113; 35; 120]. Our results indicate that the BLR algorithm outperformed ADTree in the landslide modeling process and susceptibility map assessment. BLR is an LR method, within the Bayesian paradigm, that includes a posterior distribution function for evaluating each landslide conditioning factor. BLR also offers several advantages that make it as a robust algorithm for modeling: (i) it can estimate probability intervals of landslide occurrence; (ii) it can be used with small samples, as it does not rely on large-sample approximations; (iii) available prior information about regression coefficients can be incorporated in the Bayesian model; and (iv) multi-level data or models are particularly suited to the hierarchical structure of Bayesian modeling [121; 122]. The performance and prediction accuracy of the BLR model has been confirmed and reported, not only for landslide modeling [65; 88], but also for flood [123] and land subsidence [92] susceptibility mapping. ADTree has been suggested and used by some environmental researchers [53; 92; 124]. An advantage of using ADTree is that it has the fastest induction time for domain problems with few discriminative features [125]. Moreover, it has been successfully used as a base classifier in coupled ensemble models, including multiboot (MB), bagging (BA), random subspace (RS), and rotation forest (RF) for landslide susceptibility mapping [51; 78]. Our results will be useful to landslide hazard managers, decision-makers, and researchers when selecting the most appropriate models for landslide susceptibility mapping. However, we acknowledge the limitations of the present study, largely uncertainties in input data. For example, results can differ depending on the sample size and raster resolution. [PERSON] et al.