## 1 Introduction  
Landslides are common geological hazards caused by multiple factors including landform [1; 2], geological evolution [3], groundwater [4], land use type [5], precipitation [6; 7], irrigation [8], earthquake [9], engineering construction [10], and climate change [11; 12; 13]. To avoid casualties caused by landslides and guarantee the stable development of mountainous areas, it is critical to determine a control and prevention scheme for landslides in a region. Generally, regional landslide susceptibility maps are beneficial to mitigate the effects of landslide hazards. At present, various methods have been proposed and introduced into landslide susceptibility mapping. The existing modeling approaches can be put into two categories: qualitative approaches and quantitative approaches [14; 15]. In recent years, conventional qualitative approaches have been gradually abandoned by many researchers due to the risk that expert opinion can make the results stray from objective reality [16]. Compared with qualitative approaches, quantitative approaches are mainly based on the hidden information of objective data instead of subjective experience. Additionally, quantitative approaches mainly include traditional mathematical statistic methods, deterministic models, and some state-of-the-art machine learning algorithms. For traditional statistical methods, the probability-frequency ratio (FR) [17; 18], weight of evidence (WoE) [19; 20], statistical index (SI) [21; 22], index of entropy (loE) [23; 24], certainty factors (CF) [25; 26; 27], evidential belief function (EBF) [28; 29; 30], and logistic regression (LR) [31; 32] models have been extensively adopted in landslide susceptibility mapping. However, one limitation for all traditional statistical methods is that some hypotheses exist [33]. In deterministic models, the detail characteristics of slopes are necessary to construct the calculation model [34]. Although deterministic models conform to basic physical laws of landslide, these models are not very suitable for regional landslide susceptibility assessments due to the complex process of modeling and computing [34]. In the past decade, with the rise of machine learning and data mining, a number of relevant algorithms have been developed for landslide susceptibility zonation [35; 36; 37; 38; 39]. For instance, the logistic regression model (LRM), artificial neural network (ANN), support vector machine (SVM), and decision tree (DT) were the top four machine learning algorithms in landslide susceptibility mapping during the period of 2005-2016 [16]. It is clear that machine learning algorithms improve the prediction accuracy of regional landslide occurrence, but the generalization performance of single classifiers still needs to be promoted [40]. In this way, a series of ensemble approaches have recently become more and more popular in geo-hazard susceptibility mapping [37; 41; 42; 43]. In terms of ensemble approaches, several single classifiers have been combined using ensemble frameworks including random subspace [44], random forest [45; 46], Bagging [47], AdaBoost [48], MultiBoost [49], and so on [37; 50; 51; 52]. Currently, some novel ensemble techniques have been proposed and applied in landslide susceptibility assessment, flood susceptibility mapping, and groundwater potential analysis [41; 53; 54]. Additionally, the excellent performance of ensemble algorithms on predictive ability and generalization capacity has also been proven. For example, [PERSON] et al. [55] compared four ensemble-based machine learning models (AdaBoost, LogitBoost, Multiclass Classifier, and Bagging) with the traditional frequency ratio model (FRM) in the task of landslide susceptibility mapping. Furthermore, the results demonstrated that all of the AUC values of the four ensemble-based machine learning models were higher than that of FRM.