## 1 Introduction  
Landslides are global geohazards that are responsible for substantial death and injury [1], as well as damage to the natural and built environment [2]. Previous studies reported that landslides annually cause more than 4300 fatalities and global economic losses of tens of billions of USD [3]. The distribution maps of landslide susceptibilities are essential tools for mitigating the devastating effects of landslides. These maps provide engineers and managers with operational guidance and reference for making timely decisions to proactively manage unstable terrain and mitigate the effects of potential landslides [4]. However, the preparation of reliable maps remains challenging because landslides are influenced by an interplay of different climate, geologic, geographic, and anthropogenic factors [5]. Landslide susceptibility can be modeled using a variety of GIS-aided methods, including regression logistics [6], simplified statistical/probabilistic frequency ratios [7], analytical hierarchy process [8], statistical indices [9], weight of evidence [10], evidential belief functions [11], certainty factors [12], and geographically weighted regression [13]. Another suite of approaches is machine learning methods, which are known as methods with an advantage for processing large datasets that exhibit non-linear and complex relationships, and that are typically associated with environmental problems, particularly natural hazard issues such as floods [14], wildfires [15], sinkholes [16], drought [17], earthquakes [18], gully erosion [19], and land subsidence [20; 21]. The methods can recognize the discrepancy between historical records and different landscape-level variables to predict future events [22]. The models derived from machine learning that have been tried for modeling and mapping landslide susceptibility include artificial neural networks (ANN) [23], neuro-fuzzy [24; 25; 26], support vector machines (SVM) [22], decision trees [14; 27], index of entropy [28; 29], fuzzy Shannon entropy [30], logistic model trees (LMT) [31], Bayesian logistic regression (BLR) [32], reduced error pruning trees [33], classification and regression trees [34], kernel logistic regression [35; 36], Naive Bayes trees (NBT) [37], and alternating decision trees [20; 38]. The methods differ in the matter of the input process, modeling, output process, and prediction success, and all of them involve uncertainties in the modeling process [39; 40]. Another approach that has been proven effective in many fields of science, including landslide modeling and mapping, is ensemble modeling [41], which emerged in the early 1990s [42]. In terms of accuracy and robustness, ensemble methods have outperformed single model methods [43]. Recent advances in machine learning algorithms and computational powers, coupled with an increase in the availability of high-resolution data, now allow a wider use of ensemble modeling in landslide prediction [44]. Examples of popular ensemble techniques include Bagging, Rotation Forest, Random Subspace and MultiBoost. These methods, in turn, have been used to enhance the performance of base NBT [45], BLR [32], LMT [37], ANN [46], random forest [47], J48 decision tree [48], SVM [49], stochastic gradient descent [41], and alternating decision tree [50] methods. Because landslides have such serious impacts on society, there is a need to continually improve the predictability of landslide susceptibility models for different environmental settings. The key objective of our modeling research is to improve the reliability of landslide modeling and the effectiveness of landslide susceptibility maps for land-use planners and decision makers. To achieve this, we use a Hyperpipes (HP) model in an ensemble learning framework to predict landslide susceptibility in the Ha Giang province, Vietnam. We couple the HP model with AdaBoost, Real AdaBoost, Bagging, Dagging, and Decorate ensemble learners.