## 5 Discussion  
Landslide susceptibility mapping aims to detect the vulnerable areas potentially threatened by landslide geo-hazards. Over the years, advancements in computer technology, the development of machine learning methods, the availability of multi-source data such as GIS data and remote sensing data, and the accessibility of convenient software have facilitated the conduction of landslide susceptibility assessment. In this context, how to improve the accuracy of assessment results has become the main concern of researchers [5]. Recently, ensemble techniques have proved to be able to provide a more accurate solution in landslide susceptibility modeling. In this work, we attempted to use a meta-learning method, namely the stacking ensemble method, to integrate multiple different algorithms. When the predictive accuracy was used as the measure, the ensemble of the SVM-ANN-NB-LR was found to be superior to all single algorithms. The advantage of stacking is reflected in that the two-level framework can learn more classification information, and meta-learner in the high level is capable of correcting bias generated in the former level [80, 81]. In this study, the stacking ensemble model showed improved performance against their components, and can be considered as a promising method for landslide susceptibility mapping. In general, combining several algorithms enables an ensemble model to gain better performance [29]. However, is it necessary to use all of them to constitute the ensemble when multiple predictors are available? For landslide susceptibility modeling, this needs to be investigated. In the field of machine learning, [PERSON] [82] introduced the concept of the selective ensemble, that is, making appropriate choices from multiple solutions and then combining those selected solutions to obtain the final decision. Similarly, [PERSON] et al. [83] proposed the selecting base classifiers on bagging (SBCB) algorithm, which proved to be superior to the original bagging method. Determining suitable base learning algorithms is important to examine the \"optimal\" landslide susceptibility assessment when ensemble modeling is the case. [PERSON] et al. [44] found the combination of LDA-QDA-LR had a better generalization capability than the combination of LDA-QDA-LR-ANN because the ANN overfitted the landslide information in the training stage. [PERSON] et al. [24] investigated the SVM, ANN, and ME accompanied by their possible ensembles of ANN-SVM, ANN-ME, SVM-ME, and ANN-ME-SVM for landslide susceptibility mapping. They concluded that synergic interaction of two best single models, namely the SVM and ANN, constituted a stronger bond compared with remaining modeling methods. The ensemble models containing the ME showed moderate performance due to the underperformance of the ME model. This result is quite consistent with a gully erosion modeling study which reported that single models with good performance such as SVM and ANN played an improver role in ensemble modeling, while the relatively weak model (e.g., ME) acted as a depriver [84]. A powerful model tends to add positive properties to subordinate models. To put it in another way, the outperformance of the ensemble depends mainly on good components. Therefore, the best base learner, e.g., the ANN used in this study, should be included in ensemble modeling to maintain the outperformance. In contrast, the base learner with a relatively poor performance that may play a reducer role seems to be inappropriate for constituting the ensemble. However, all base learners used in this study achieved good predictive accuracies. Here, \"slightly weak\" is a relative concept; the threshold for positive synergy among base learners may be difficult to determine [24]. In addition, it is also questionable to explain the extremely similar behaviors between the SAN model and the ANL model only judged by the component's performance, because removing SVM or LR from the initial ensemble of the SVM-ANN-NB-LR has an equal effect in the case where the SVM outperformed the LR.