## 6 Concluding Remarks  
This paper proposes a new modeling approach that is a hybrid intelligence of BE-LMTree for landslide susceptibility mapping with a case study at URRB. According to current literature, the BE-LMTree model has not been used for landslide modeling. For this purpose, the GIS database for the URRB area has been established, which contains a total of 255 historic soil-mixed-boulder slides and eight geo-environmental factors. These factors checked their merits to landslide using the Pearson correlation. The GIS database was then used to construct and verify the BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrices and several model measures.  
The results in this study point out that the new approach of the BE-LMTree could help to model landslide susceptibility with desirable prediction capability. When compared to the support vector machines (SVMC), a recognized benchmark in landslide modeling, the proposed BE-LMTree model presents a better performance. Therefore, the BE-LMTree is a new promising tool that could be used to enhance the quality of landslide susceptibility mapping.  
For the case of the LMTree, this technique has been recently investigated for landslide susceptibility mapping with promising results i.e., in [50], the performance of the LMTree model in this research is lower than that of the SVMC model and the BE-LMTree model (Figures 4 and 5). Therefore, it could be concluded that the integration of the BE and the LMTree has significantly improved the  
Figure 12: Landslide susceptibility map for the study area using the proposed BE-LMTree model.  
quality of the LMTree model. This is due to the stability and robustness of the BE procedures itself with the ability to reduce variances [101]. This finding agrees with [51], who concluded that the performance of the landslide model is enhanced with the use of ensemble frameworks.  
The main disadvantage of the proposed approach is that the quality of the BE-LMTree model is heavily controlled by the minimum number of samples (NS) that is used for growing the LMTree and the number of bootstrap subsets (BS) used in the BE. In this research, NS and BS were determined using an empirical test. Although the NS and the BS found results in the high performance BE-LMTree model, however these do not warrant them being the optimal parameters. Therefore, the performance of the BE-LMTree model may be further enhanced if optimization algorithms are considered to integrate in the model. In addition, the BE-LMTree may create a complex forest trees i.e., 50 trees in this research. Therefore, the interpretation of the BE-LMTree model may be complicated. Despite the aforementioned limitations, the BE-LMTree can be considered as a new and valid tool for landslide susceptibility modeling.  
X.L.T., M.M., Y.K., V.R., G.Y., X.Q.T., and T.H.D collected data and processed input data. [PERSON], [PERSON], [PERSON], and [PERSON] carried out the modeling process and wrote the paper.  
This work is supported by: (i) the Scientific Research Project 02/2012/HD--HTQPSP funded by Ministry of Education and Training, Vietnam; and (ii) by the Scientific Research Project DTNCB-DHUD.2012-G/01 funded by NAFOSTED, Ministry of Science and Technology, Vietnam. This work is supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Minister of Science and ICT of Korea.  
We would like to thank three anonymous reviewers for their valuable and constructive comments on the earlier version of the manuscript.  
The authors declare no conflict of interest.