## 1 Introduction  
Landslides are destructive geological hazards that may result in serious economic damage and human losses all over the world [1]. Thousands of landslides occurred in January 2011 in Rio de Janeiro causing more than 1500 people to die [2]. China has suffered much from natural hazards in the past decade. On 24 June 2017, a rocky landslide occurred in Maoxian County, Sichuan Province, China, causing the whole village to be buried and the death of 83 people [3]. On 7 August 2010, catastrophic debris flows occurred in Zhouqu, China, leading to 1765 deaths [4]; among these geohazards, landslides occurred most widely and accounted for the highest proportion. In 2018, 1613 landslides occurred, accounting for 55% of the total geological disasters [5], and the economic loss exceeded 2 billion CNY. Three Gorges Project, the largest hydropower station in the world, has formed a 660 km long backwater area after impoundment. The highest water level in the Three Gorges Reservoir area (TGRA) has risen to 175 m since 2009, with an annual variation of 30 m. The frequent changes of water level have significantly changed the geological environment of the TGRA. This has led to the reactivation of certain old landslides and the occurrence of new landslides. These landslides seriously threaten the safety of local residents and their property. For instance, Qianjiangping landslide and its associated 30 m impulse wave occurred shortly after the initial impoundment of TGRA in July 2003, causing 24 deaths, destroying 346 houses, and capsizing many ships [6]. Shanshucao landslide occurred in September 2014, which was triggered by both the rising water level of the TGRA at high speed and rainfall, causing the Daling Power Station and part of the G348 national highway (about 200 meters long) to slide into the river [7]. Hence, considering the number of disasters and the damage they caused, it is crucial and urgent to monitor the TGRA. Landslide susceptibility modelling can be considered as the initial step towards a landslide hazard and risk assessment, and can notably improve land-use planning [8]. At present, the landslide susceptibility models can be divided into qualitative models and quantitative models. Qualitative models include inventory-based models and knowledge-driven models, whereas quantitative models mainly include data-driven models and physically based methods [9]. Qualitative models are based on simple expert knowledge, which is easier to obtain but greatly affected by subjective factors. Physically based models can simulate the failure process of landslides, but it is not practical for large-scale areas in terms of its necessary of plenty of parameters [10]. At present, data-driven models have been widely used, the accuracy of which have been greatly improved because of the high data quality. The data-driven models include information value model [11], weight-of-evidence [12], logistic regression (LR) [13], artificial neural network (ANN) [14; 15; 16], support vector machine (SVM) [17; 18; 19], decision tree [20], and classified and regression tree (CART) [21], among others. Among those models, machine learning methods have become popular in landslide susceptibility modelling because of their good non-linear prediction ability. The performance of machine learning models may vary in different cases. In the TGRA or other landslide-prone areas, there is no universal agreement for the selection of landslide susceptibility models until now. Therefore, it is necessary to analyze and compare landslide susceptibility models. Landslide development is jointly influenced by many factors, and different causal factors have different ways of influence [10]. Some inconsequential factors may contribute less to improving the accuracy of susceptibility modelling than the errors caused by noise, thus reducing the accuracy of modelling. The important causal factors should be selected and the less important causal factors should be eliminated to improve the modelling accuracy of landslide susceptibility [22; 23].