## 7 Discussion  
The objective of spatial landslide modeling is to generate a valid and accurate susceptibility map [44; 89]. So far, many models have been built for landslide susceptibility mapping over the past few decades, out of which, machine learning algorithm-based ensemble models have received more attention in recent years [44]. In the present study, a novel hybrid intelligent model, namely, MBNBT, was introduced for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province (Vietnam). The performance of the MBNBT was compared with benchmark models such as MLPN, SVM, and single NBT. Fifteen landslide conditioning factors: slope, aspect, profile curvature, curvature, plan curvature, elevation, distance to rivers, river density, distance to roads, road density, distance to faults, fault density, land use, lithology, and rainfall were selected for landslide modeling based on site condition and experience. The ORAE technique was used to select the most important conditioning factors for the landslide spatial prediction. Considering the acceptable performance of the models in the training and test stages, the ORAE can be considered a powerful technique to select important factors to enhance the power prediction capability of base/individual models while decreasing the noise and also reducing over-fitting problems. The results of the ORAE method showed that all fifteen studied factors were contributing landslide occurrences, but road density was the most important factor, as most of observed landslides in the study area were located near the roads. However, we want to make it clear that roads themselves are not responsible for landslides, but that the excavation for the roads creates instability in the surrounding ground mass, which leads to landslides [13; 27; 44]. Thus, the human interface in changing the existing geo-environmental conditions of the area plays an important role in landslide occurrences. A comparative study of the predictive capability of the models using SEN, SPC, ACC, MAE, RMSE, and AUC methods indicated that the proposed novel model MBNBT was the best model for landslide spatial prediction. However, other models also gave a reasonable performance. The results of the MLPN model showed that the performance of this model was better than the SVM and NBT models, which is in agreement with the finding of [PERSON] and [PERSON] [91] and [PERSON] et al. [92]. Thus, the MLPN model can be used successfully for the spatial prediction of landslides. [PERSON] et al. [93] reported that the MLPN had good predictive performance. However, the MLPN data sampling methods employed significantly affected the performance of this model, especially when the training dataset was small [94], and this problem is considered a major deficiency of MLPNs [93]. The performance of the SVM model was reasonable in landslide susceptibility mapping [95; 96]. The present study showed that the NBT had the lowest performance compared to the other models. This was because the NB-based algorithm was based on the independent assumption among predictor variables that would affect its predictive accuracy [97]; therefore, performance of NBT depends on the independence assumption [98]. Analysis of the model study results showed that the proposed MBNBT was a better model because hybrid intelligence is considered more effective than single classifiers [96]. The MBNBT takes advantages of the combination of two machine learning methods, namely, MB and NBT. More specifically, the MB used in MBNBT is known as an effective ensemble method which is able to improve the classification accuracy of single classifiers like NBT [52]. Likewise, the NBT used in MBNBT is also a good and encouraging method in landslide prediction [98] and has the advantages of both DTs and NB [55]. On the other hand, the input dataset used for MBNBT was optimized during the training process; therefore, it helped to increase the classification accuracy of the MBNBT compared with other single classifiers (MLPN, SVM, and NBT).