## 1 Introduction  
Landslides are a devastating natural hazard, which cause an enormous loss of properties and human life [1; 2; 3]. Many efforts have been made to correctly predict landslides, and thus, reduce landslide damages all over the world. However, landslide problems are still a great challenge to governmental agencies and hazard managers, as landslides are a phenomenon of high complexity [4] and any method for landslide prediction is dependent on the complexity of the cartographic data at the landscape level [5; 6] for each particular analysis [7]. Researchers throughout the world have attempted to establish relationships between past landslide occurrences and future landslides [8; 9; 10]. Landslide prediction studies are generally carried out through mathematical models by analyzing statistical relationships between the occurrences of past landslides and landslide affecting factors [11; 12; 13; 14].  
Several models and techniques have been proposed and applied for spatial prediction of landslides all over the world. These models include quantitative and qualitative models [15]. The most popular model among quantitative models is Logistic Regression (LR), which is based on statistical theory [16; 17]. This model is considered a benchmark model for comparative study [15; 18; 19; 20]. Another popular quantitative model is Support Vector Machines (SVM). Among the qualitative models, Analytical Hierarchy Process (AHP), which is one of the multi-criteria decision-making (MCDM) methods, is the most popular [21]. This model is based on expert knowledge, thus the results may be biased [22].  
Nowadays, some of the quantitative models that use machine learning algorithms are considered better than qualitative models [23; 24; 25], such as SVM [26; 27], artificial neural networks (ANN) [28], and naive bayes (NB) [29; 30]. These models are also considered more efficient than conventional models such as frequency ratio [31; 32], weight-of-evidence (WOE) [31], and AHP [33]. In addition, some of the hybrid machine learning models achieved higher performance in landslide susceptibility mapping [34; 35]. Ensemble models with meta and optimization algorithms decrease the noise and over-fitting problems [18; 36]. Hybrid models which are used in natural hazard studies include adaptive network-based fuzzy inference systems (ANFIS) based on the genetic algorithm (ANFIS-GA) [37; 38], relevance vector machines optimized by the imperialist competitive algorithm (SVR-ICA) [39], alternating decision tree based on multiboost, bagging, rotation forest, and random subspace [36] and the radial basis function artificial neural network optimized by rotation forest (RBFRF) [15], ANFIS based on the differential evolution (ANFIS-DE) [37], ANFIS based on the biogeography-based optimization (ANFIS-BBO) and BAT algorithms (ANFIS-BAT) [40], and ANFIS based on imperialist competitive algorithm (ANFIS-ICA) and firefly algorithms (ANFIS-FA) [41].  
In the present study, a novel hybrid intelligent model named MBNBT, which is combination of multiBoost (MB) and naive bayes trees (NBT) classifier, was proposed for the spatial prediction of landslides in Mu Cang Chai District, Yen Bai Province, Vietnam. Other models based on popular machine learning techniques, namely, multiple perceptron neural network (MLPN), SVM, and single NBT were used for comparison and evaluation. Validation of the models was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics (ROC) curve (AUC). In this study, we used ArcGIS software version 10.2 to prepare the data and maps, and Weka software version 3.9 for constructing and validating the models.