###### Abstract  
Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas. Keywords:landslide; meta classifier; prediction power; China +
Footnote †: journal: Medical  
to human life and the environment cannot be ignored [2]. Due to the potential economic, social, environmental, and health impacts of landslides, policy makers pay attention to landslide hazard zonation maps to identify sensitive areas and sustainable locations for future development [3]. Globally, around 66 million people are living within the areas at high risk of landslides [4]. For example, in the United States, the annual financial losses by landslides is estimated about USD $1.5 billion. Most areas in China are mountainous, so are susceptible to landslides, and direct and indirect economic losses induced by landslide account for more than $20 billion every year, endangering the lives of local people [5]. Due to the damage caused by landslides, the first step in landslide hazard assessment is preparation of landslide susceptibility maps (LSMs) on a regional scale [6]. Statistical models are most commonly used in landslide susceptibility mapping, which are based on the analysis of the relationships between influencing factors and existing landslides [7]. In these statistical approaches, bivariate and multivariate statistical techniques are used for landslide susceptibility mapping throughout the world, including frequency ratio [8; 9; 10], index of entropy [11; 12; 13; 14; 15], bivariate statistical analysis [16], multivariate adaptive regression spline [17], analytical hierarchy process [18; 19], statistical index [20; 21], weight of evidence [13; 21], evidential belief function [22; 23], certainty factor [24; 25], and logistic regression [26; 27; 28; 29]. In addition to the above mentioned development of LSMs, various data mining techniques have been introduced for LSM, for example, neuro-fuzzy [30; 31; 32], artificial neural network [33; 34], kernel logistic regression [33; 35], multivariate adaptive regression spline [19; 36], decision trees [37; 38; 39; 40], support vector machines [41; 42], random forest [23; 43], adaptive neuro-fuzzy inference system [44; 45; 46], and naive Bayes [47; 48]. However, the best method for creating LSMs is still under discussion. Researchers have reported that hybrid models produce better outcomes than individual machine learning techniques, so they are thought to be best for creating the best LSMs [49; 50]. Therefore, the aim of this study was to introduce a new hybrid bagging-based two-class kernel logistic regression (BKLR) for landslides susceptibility mapping.