## 1 Introduction
Shallow landslides with a small thickness (generally less than 2 m) are easily induced by rainfall because frequent rainfall events determine the progressive infiltration of the rainwater to the deepest soil levels [22]. It was found that the depth of soil and the inclination of the slope are the main controlling factors of slope stability, and vegetation roots can improve the stability of a slope [23]. An infinite slope stability analysis (ISSA) model, involving some factors of shallow landslides, was established by [PERSON] [24]. The stability of shallow slopes is highly sensitive to the effective cohesion force (\(c\)), piezometer height (\(h\)), inclination of the slope (\(\beta\)), and average sensitivity to soil depth (\(z\)), and it is relatively insensitive to the effective angle of internal friction (\(\varphi\)) and soil unit weight (\(\gamma\)). The process sensitivity index, which was proposed by combining averaging methods and a variance-based global sensitivity analysis, was used to solve the problem; the model consists of multiple process-level sub-models [25]. An ISS (infinite slope stability) model applied by [PERSON] et al. [26] showed that slope inclination and soil depth have the greatest influence on the output of the infinite slope stability model. Some researchers adopted the stacking ensemble technique, combining a radial basis function (RBF) with the random subspace (RSS), attribute selected classifier (ASC), support vector machine (SVM), least squares support vector machine (LS-SVM) [27], cascade generalization (CG) [28; 29], artificial neural network (ANN), gradient-boosting decision tree (GBDT) [30], recurrent neural network (RNN), convolutional neural network (CNN) [31], and other machine learning methods. [PERSON] et al. [32] integrated a hierarchical uncertainty framework with a variance-based global sensitivity analysis to deal with a large number of input factors, such as slope, aspect, elevation, curvature, slope length, and valley depth. Many other factors were used to develop the ensemble model's package landslide samples [33; 34; 35]. The seismic pseudo-static stability of a rock wedge was studied based on the nonlinear Barton-Bandis criterion. The sensitivity analysis showed that the parameters related to the strike of the joint plane have a greater influence than others [36]. Since water level fluctuation is a critical trigger factor for a landslide, some slope stabilization techniques can be used to alleviate this problem. The controlling techniques for landslide prevention engineering include anti-slide pile and wall systems, surface and underground drainage systems, and prestress systems [37]. Stabilizing piles are widely used for increasing the safety conditions of thrust-type slopes, which mainly control the deformation and failure process of the upper sliding mass. A stabilizing pile is a passive reinforcement measure [38]. Based on the results of a model test and field test on stabilizingpiles in China, the distribution functions of the landslide-thrust and the resistance of soil or rock have been deduced [39]. [PERSON] et al. combined the water balance equation with the motion equation to evaluate and predict the rainfall-induced movements of landslides in the presence of stabilizing piles [40]. [PERSON] et al. established an in situ multi-field information monitoring platform to monitor the multi-field dynamic process of the Majiagou landslide with stabilizing piles. The monitoring results showed that the deformation of the experimental piles was influenced by the reservoir operation [41]. Current drainage systems can be divided into surface water drainage systems and subsurface drainage systems [42]. The drainage tunnel can control the decrease in the depth of the groundwater table and reduce the pore water pressure of landslides to promote the stability of slopes [37; 43].