## 1 Introduction  
Landslides are some of the most fatal natural disasters worldwide, which greatly influences human life and development of infrastructures in underdeveloped countries [1; 2]. For instance, about 15,000 landslides happened in China between 2015 and 2017, which caused the economic loss of about one million US dollars. Therefore, it is urgent to conduct studies on landslide susceptibility mapping (LSM) and it is a primary and practical tool for mitigation and risk assessment [3]. In recent years, the assessment and management of landslides have resulted in significant reduction in losses in several countries [4; 5], where correlation on developments of landslide and influencing factors is clarified. [PERSON] et al. [6] found that the human activities have some influence on sediment connectivity of landslides and the intensity of their occurrence. [PERSON] et al. [7] inferred that large landslides may locally inhibit incision by changing the river channels' slope and width and riverbed characteristics. [PERSON] et al. [8] found that landslide movement is a non-uniform and non-rigid body motion which uses speckle and grayscale feature search methods. [PERSON] et al. [9] found that a rotated ellipsoid trend surface and a random field of residuals were influenced by the spatial maximum rolling rainfall [10]. Rainfall can enlarge the weight of soil, alter the pore water pressure and decrease the strength of soil, and trigger other types of landslides [11; 12; 13]. Although researchers are making tremendous efforts, prediction of landslide with high accuracy is still challenging, especially in the regional scale [14; 15]. The quality of landslide inventory map has improved significantly with the use of new remote sensing techniques. For example, [PERSON] et al. [16] analyzed pixel-level and object-level landslide detection methods using high resolution remote sensing images, where major landslides were easy to recognize with a few errors. However, quality of the landslide prediction is strongly dependent on the modelling approach [17], therefore, various data-driven approaches have been considered, including logistic regression [18], neural networks [19; 20; 21; 22; 23; 24; 25], support vector machine (SVM) [18; 26; 27; 28; 29; 30; 31], relevance vector machine [32], least squares SVM [33; 34], decision trees [35; 36; 37; 38; 39], logistic model trees [40], random forest [38; 41; 42], gene expression programming [43]. However, no method or technique is the best for landslide modelling at the regional scale for all regions. One of the difficulties in landslide modelling at the regional scale is to handle landslide influencing factors which are usually derived from various sources with different scales that may contain uncertainties and imprecisions [44]. This requires new modelling methods other than traditional data-driven approaches, which have abilities to deal with the above issues and enhance the model performance. Literature review shows that some approaches have been proposed, i.e., fuzzy k-means [45], fuzzy logic [46; 47], neural fuzzy [48; 49; 50; 51], and fuzzy k-nearest neighbor [44], and among them, the neural fuzzy, which is a hybrid of artificial neural network (ANN) with fuzzy logic, has proven its efficient for landslide modelling in term of prediction accuracy [52; 53]. However, performance of the neural fuzzy model is strongly influenced by its weights for membership function and finding these optimal weights is still a challenging task [54]. Recent developments of machine learning (ML) have introduced new optimization algorithms, which could be used for optimizing weights for membership function of the neural fuzzy model.