## 1 Introduction  
Landslides are severe natural disasters and are responsible for losses of life and property [1]. In 2018, approximately 105 deaths and direct economic losses of US$212 million were caused by landslides along with other disasters in China ([[http://www.cigem.cgs.gov.cn](http://www.cigem.cgs.gov.cn)]([http://www.cigem.cgs.gov.cn](http://www.cigem.cgs.gov.cn))). To reduce these harmful impacts, an assessment of slope conditions must be performed. Landslide susceptibility mapping is an efficient method to identify vulnerable areas, and is used to carry out early risk assessments [2]. Numerous approaches, including the heuristic and physical-based methods, have been applied for landslide susceptibility mapping in the last few decades, and their advantages and disadvantages are well summarized in previous studies [3; 4; 5]. Heuristic methods usually fail to provide a quantitative evaluation of the spatial likelihood of landslide occurrence [6]. Physical-based methods are characterized by computing requirements and high costs for collecting necessary data, and are not suitable for large-scale susceptibility mapping [7]. Recently, machine learning methods have been shown to be effective to address the issue of landslide spatial prediction [8]. Relevant work has focused on logistic regression (LR) [9, 10], the fuzzy logic method [11, 12], maximum entropy (ME) [13], artificial neural network (ANN) [14, 15, 16], Bayesian network [3], naive Bayes (NB) [17], support vector machine (SVM) [18, 19, 20], classification and regression tree [21, 22], and logistic model tree [23], etc. Machine learning algorithms treat landslide-related conditions as input to predict where landslides are probably to occur without the constraint of statistical assumptions. Specifically, state-of-the-art machine learning techniques such as SVM and ANN are the most popular landslide modeling methods. They have achieved promising predictive capability for landslide modeling and are usually used as benchmarks to test novel methods [24, 25]. NB and LR have also been widely employed for landslide susceptibility mapping as they are easily implemented [2]. Yet, there is still no general agreement on which method is better because the single or simple hypothesis space of learning algorithms shows difficulties in meeting all case scenarios, as the used data change [26]. Modeling of landslide susceptibility still faces challenges due to the complex nonlinear relationship between conditioning factors and landslide occurrences [27]. Achieving higher accuracy is the focus for landslide susceptibility assessment. More recently, the ensemble machine learning method has proved to be able to provide an improved solution for landslide susceptibility modeling [28]. Ensemble methods can expend the hypothesis space of the fitting function, thus providing better prediction than the single algorithms [29, 30]. In general, the single algorithm used to constitute ensemble is called the \"base learner\" algorithm and can be homogeneous or heterogeneous. Several landslide studies have investigated meta-learning techniques for assembling homogeneous base learners [31, 32, 33, 34, 35, 36]. In these studies, the bagging [37], boosting [38, 39], random forest [40], and rotation forest [41] were most widely applied. Additionally, combination schemes based on heterogeneous landslide models were also applied for landslide susceptibility mapping successfully, such as ANN-Bayes analysis [42], ANN-fuzzy logic [43], combinations based on linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), LR, and ANN [44]; the combination of the statistical index and adaptive neuro-fuzzy inference system [45]; and combinations based on ANN, MaxEnt, and SVM [24]. Exploring novel ensemble modeling methods is of significance for landslide susceptibility assessment. In this paper, for the first time the stacking ensemble method based on multiple machine learning algorithms was used to conduct landslide susceptibility mapping in the Lushui area, southwest China.