## 3 Results
### PSO-MLP Model for LSP  
The PSO algorithm was applied to optimize the parameter selection of the MLP model with the mini-batch gradient descent algorithm. First, the PSO algorithm was initialized; for example, the particle population was set to 100, the maximum number of iterations was set to 35, the initial  
Figure 5: Flow chart of the PSO-MLP model.  
velocity and position of each particle were randomly determined, and the other related parameters were set to default values. Meanwhile, the dimensions of both velocity and position were set to four because there were four parameters (learning rate, learning delay rate, the momentum of the mini-batch gradient descent algorithm, and the number of neurons in the hidden layer of the MLP model) that needed to be determined.  
Secondly, several initial parameters of the MLP model were assigned: the number of the hidden layer was set to 1; the learning rate, learning delay rate, and momentum of mini-batch gradient descent were respectively set to 0.001-0.2, 0.001-0.5, and 0.05-0.95; and the number of neurons in the hidden layer ranged from 5 to 35. In addition, some other parameters of MLP were set to default values. Thirdly, the PSO-MLP model was trained and tested based on the datasets introduced in Section 4.3. The PSO-MLP model-building process showed that its optimal parameters were a learning rate of 0.006, a learning decay rate of 0.004, a learning momentum of 0.78, and a hidden-layer neuron number of 23.  
Finally, a landslide susceptibility map was produced based on the LSI values calculated by the PSO-MLP model, as shown in Figure 6a. This landslide susceptibility map was divided into five levels using the natural interval point method [63]: very high (10.2%), high (15.7%), moderate (25.7%), low (24.8%), and very low (23.6%) susceptibility levels (Table 2). In general, the high and very high landslide susceptibility levels were mainly distributed in areas within a close distance to river networks and having a relatively low DEM, moderate slope, high population density, and active human engineering building. On the contrary, the low and very low landslide susceptibility levels were mainly located in zones far from river networks and having a high DEM, gentle or steep slopes, high vegetation cover rate, and few human activities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Class**} & **Total Grid** & **Proportion** & **Landslide Grid** & **Proportion** & \multirow{2}{*}{**FR Values**} \\  & & **Number** & **(\%)** & **Number** & **(\%)** & **(\%)** \\ \hline \multirow{8}{*}{PSO-MLP} & Very low & 414,852 & 23.6 & 36 & 1.3 & 0.056 \\  & Low & 435,746 & 24.8 & 209 & 7.7 & 0.311 \\  & Moderate & 451,412 & 25.7 & 477 & 17.6 & 0.685 \\  & High & 275,386 & 15.7 & 726 & 26.8 & 1.710 \\  & Very high & 179,941 & 10.2 & 1261 & 46.5 & 4.546 \\ \hline \multirow{8}{*}{MLP-only} & Very low & 485,954 & 27.7 & 107 & 3.9 & 0.143 \\  & Low & 402,491 & 22.9 & 224 & 8.3 & 0.361 \\ \cline{1-1}  & Moderate & 398,323 & 22.7 & 452 & 16.7 & 0.736 \\ \cline{1-1}  & High & 296,913 & 16.9 & 763 & 28.2 & 1.667 \\ \cline{1-1}  & Very high & 173,656 & 9.9 & 1163 & 42.9 & 4.344 \\ \hline \multirow{8}{*}{BPNN} & Very low & 451,506 & 25.7 & 72 & 2.7 & 0.103 \\  & Low & 393,108 & 22.4 & 198 & 7.3 & 0.327 \\ \cline{1-1}  & Moderate & 437,828 & 24.9 & 467 & 17.2 & 0.692 \\ \cline{1-1}  & High & 282,685 & 16.1 & 719 & 26.5 & 1.650 \\ \cline{1-1}  & Very high & 192,208 & 10.9 & 1253 & 46.3 & 4.229 \\ \hline \multirow{8}{*}{IV} & Very low & 417,155 & 23.7 & 81 & 3.0 & 0.126 \\ \cline{1-1}  & Low & 400,663 & 22.8 & 222 & 8.2 & 0.359 \\ \cline{1-1}  & Moderate & 468,615 & 26.7 & 549 & 20.3 & 0.760 \\ \cline{1-1}  & High & 302,688 & 17.2 & 772 & 28.5 & 1.655 \\ \cline{1-1}  & Very high & 168,214 & 9.6 & 1085 & 40.1 & 4.184 \\ \hline \hline \end{tabular}
\end{table}
Table 2: FR values of landslide susceptibility levels classified by the four models.