## 1 Introduction  
A landslide is a type of very serious natural hazard that occurs worldwide and results in immense losses in human life and property [1; 2; 3]. Much attention has been paid by geological engineers to determine the susceptible areas where landslides are likely to occur, and landslide susceptibility prediction (LSP) and susceptibility mapping are significant technologies used to this end [4; 5].  
Along with the development of information technologies, remote sensing and the geographic information system (GIS) have gradually become data sources and spatial analysis platforms for LSP [6; 7]. Based on remote sensing and GIS, many mathematical models have been proposed to calculate landslide susceptibility indices (LSI), such as the analytic hierarchy process [8; 9; 10], weight evidence method [11], information value (IV) theory [5; 12], frequency ratio (FR) method [13; 14],logistic regression model [7; 15; 16], logistic tree model [17], random tree [18; 19], boosted tree [20], multi-criteria evaluation model [21], artificial neural networks (ANNs) [22; 23; 24], support vector machine (SVM) [25; 26; 27], and neuro-fuzzy method [28]. Although many models have been proposed for LSP, there is no model that is universally accepted and there is much room for improvement for these models.  
It is important and difficult to choose an appropriate model for LSP. Generally speaking, the related literature shows that machine learning models (e.g., ANNs and SVM) have higher prediction performances than other LSP models because machine learning models can more effectively fit and predict the nonlinear mapping relationships between landslides and predisposing factors [14; 23; 29; 30; 31]. Among these machine learning models, ANNs have been commonly and successfully introduced into the area of LSP. ANNs are robust when data noise interferes with the LSP modeling processes and their modeling processes are relatively simple, as they are based only on input-output variables. The multilayer perceptron (MLP) model, which has a feed-forward neural network structure, is one of the most popular ANNs [32]. The MLP model has been proposed for LSP by several researchers, and related LSP results have proved the validity of this model [33; 34; 35].  
However, there are still some drawbacks in the MLP model that may reduce LSP performance, which can be summarized as follows: (1) the gradient descent algorithm commonly used in the MLP model is prone to local optimization and low training speed [36; 37]; (2) several closely related structural parameters of MLP, such as the learning rate, learning decay rate, momentum, and the number of neurons in the hidden layer, are difficult to appropriately determine [38; 39]. To overcome these drawbacks, a particle-swarm-optimized MLP (PSO-MLP) model with mini-batch gradient descent has been proposed to address LSP. The mini-batch gradient descent, which can better avoid the local optimal value and reach a higher training speed compared with the conventional gradient descent algorithm, is used as the sample training algorithm of MLP [40]. The PSO algorithm, which has the advantages of excellent search capability, a global optimum, and high computational efficiency, has been successfully used in many fields [39; 41]. In this study, PSO was used to select the appropriate structural parameters of MLP.  
To sum up, a novel PSO-MLP model was developed to implement LSP in this study. Shicheng County, China was used as the study area due to the frequent occurrence of landslides in this area. Landslides result in serious socioeconomic and natural problems in Shicheng County. Hence, it is significant to map landslide susceptibility there. In addition, to verify the prediction performance of the PSO-MLP model, an MLP-only model with the conventional gradient descent algorithm, a back-propagation neural network (BPNN), and an IV model were also tested for comparison.