## 1 Introduction  
The problem of rainfall-induced landslides, which are triggered by high intense and long lasting precipitation, seems to be more serious in recent years in many regions around the world due to the effects of climate changes i.e., extreme rainfall events [1; 2; 3; 4; 5; 6; 7; 8]. The rainfall-triggered landslide is especially exacerbated in countries that are located in storm centers of the world, such as Vietnam [9], Philippines [10], and China [11]. For example, the tropical typhoon of Rasmussen caused various floods and landslides with the total damages were estimated at $7 billion [12]. It anticipates that the number of landslides in the future will continue to rise due to effects of extreme rainfall events and changes of hydrological cycles [13]. Thus, landslide has become one of the hottest subject of the research community, however, accurately prediction of landslide still is a challenging real-world problem [14]. Therefore, more researches on landslide are still urgently required for deriving better detailed knowledge of slope failure and its mechanisms for designing remedial measures. The development of a hazard map that provides detailed dimensional information of spatial distributions, temporal predictions, and destructive power of landslide is considered as an efficient tool for designing mitigation measures and management policies. However, the hazard map at the regional scale requires very detailed temporal landslide inventories that are hardly available, especially in developing countries [15]. For this context, a landslide susceptibility map (LS-map) could be alternatively employed since it helps to identify areas with high landslide probability. According to [PERSON], et al. [16], LS-map is a valuable decision-support tool that assists local authorities in land use infrastructural planning and management  
To produce susceptibility map, a variety of studying approaches has been introduced because the accuracy of the susceptibility map at regional analysis scale is controlled not only by the quality of the input maps, but also the algorithms and techniques that are employed [17]. These approaches vary from expert weighting methods to deterministic and statistical models. Evaluation of these approaches has been well presented i.e., in [PERSON] et al. [18] and [PERSON], et al. [19]. In recent years, new approaches that are based on advanced statistical and machine learning methods have been proposed i.e., fuzzy k-Nearest Neighbor [17]; fuzzy rule based models [20; 21; 22; 23]; neural networks [24; 25; 26; 27; 28; 29; 30]; support vector machines [31; 32; 33; 34; 35; 36; 37; 38]; Random Forests; metaheuristic optimized least squares support vector machines [39; 40]; Cuckoo optimized relevance vector machines [41]; Chi-squared automatic interaction detection (CHAID) [42]; tree-based algorithms [43; 44; 45; 46; 47]; and, gene expression programming [48]. The main advantage of these methods is that they are capable of involving several to a large number of variables for reliable results, and overall, these methods are able to provide better performance models when compared to those of conventional methods [43; 49; 50]. In the last years, the integration of advanced machine learning algorithms and homogeneous ensemble frameworks has been explored for landslide susceptibility modeling with promising results. For example, [PERSON], et al. [51] show that the landslide model based on a combination of functional trees with Bagging performs better than the neural network models. [PERSON] et al. [23] concluded that the hybridization of Fuzzy Unordered Rules Induction Algorithm and Rotation forest ensemble has increased the prediction performance of the landslide model when compared to the benchmark of support vector machines model. [PERSON] et al. [26] reported that the landslide model derived from a combinations of MultiBoost and Dagging with neural networks has significantly improved the prediction power of the landslide model using only the neural network.