###### Abstract  
The main purpose of this paper is to use ensembles techniques of functional tree-based bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT), rotation forest-functional trees (RFFT), dagging-functional trees (DFT)) for landslide susceptibility modeling in Zichang County, China. Firstly, 263 landslides were identified, and the landslide inventory map was established, and the landslide locations were randomly divided into 70% (training data) and 30% (validation data). Then, 14 landslide conditioning factors were selected. Furthermore, the correlation analysis between conditioning factors and landslides was applied using the certainty factor method. Hereafter, four models were applied for landslide susceptibility modeling and zoning. Finally, the receiver operating characteristic (ROC) curve and statistical parameters were used to evaluate and compare the overall performance of the four models. The results showed that the area under the curve (AUC) for the four models was larger than 0.74. Among them, the BFT model is better than the other three models. In addition, this study also illustrated that the integrated model is not necessarily more effective than a single model. The ensemble data mining technology used in this study can be used as an effective tool for future land planning and monitoring. Keywords:landslide susceptibility mapping; ensemble techniques; functional trees; bagging; rotation forest; dagging +
Footnote †: journal: Medical  
and display a set of spatial and non-spatial data about landslide conditioning factors [8; 9; 10]. Landslide susceptibility zoning mapping technology includes a variety of statistical techniques and statistical methods, including Dempster-Shafer [11; 12; 13], entropy [14; 15; 16], logistic regression [17; 18; 19], certainty factors [20; 21; 22], statistical index [23; 24], analytic hierarchy process [25; 26; 27], frequency ratio [20; 28], weight of evidence [29; 30; 31; 32], index of entropy [20; 33], multivariate adaptive regression spline [34; 35; 36], and evidential belief function [37; 38; 39]. Landslide susceptibility mapping is a typical complex nonlinear problem in a large area of a landslide research area [5]. Thus, the results obtained by statistical techniques and statistical methods may not be able to achieve satisfactory accuracy [5; 40]. Later, many researchers proposed a large number of machine learning techniques for evaluating the susceptibility of landslides, which usually have high prediction accuracy and better performance in data-driven models, such as naive Bayes [41; 42; 43], random forests [2; 44; 45; 46], artificial neural networks [47; 48; 49; 50], kernel logistic regression [51; 52], support vector machine [53; 54], and decision trees [55; 56]. However, the performance of machine learning methods is generally influenced by the quality and quantity of training data, and the dependence on modeling parameters is very high [5; 57]. So far, it is not clear which method is most suitable for landslide susceptibility mapping [5]. In recent years, hybrid technology is considered to be more effective than single technology [58]. In order to explore more reasonable and perfect research results, a variety of integrated algorithms have been developed for landslide susceptibility modeling [6], such as adaptive neuro-fuzzy inference system [59; 60], artificial neural networks-Bayes analysis [61], and Evidential Belief Function-fuzzy logic [62]. The important capability of the integrated model is that the method is more accurate in identification and greatly improves the prediction ability compared with the single machine learning model [6]. The purpose of this study is to propose and validate the ability and effect of ensemble techniques in landslide susceptibility modeling, and functional trees are selected as the base classifier to ensemble with bagging, rotation forest, and dagging models in Zichang County (China).