## 4 Secondary Index Factor Classification and Weight Calculation
### GA-BP Model Construction Analysis Based on WOE Model
#### 4.2.2 GA-BP Model Construction and Weight Calculation  
The neural network model represents a black box with nonlinear characteristics. A single-layer neural network model can uniformly approximate any continuity function [32]. Therefore, the model was constructed using a single hidden layer based on the index factor. The number of neurons in the input layer Ni was eight, and the number of neurons in the output layer No, also known as risk index, was one. However, the optimal number of hidden layers is affected by many other factors and is often difficult to determine. According to [PERSON] [33] and [PERSON] and [PERSON] [34], the recommended upper limit of the hidden node is 2 Ni + 1 and the lower limit is (Ni + No)/2. Therefore, the number of hidden layer neurons considered in this study should be in the range of 5-17. After comparing the size of the neural network mean square error, the number of hidden layer neurons was determined to be 15, based on which a neural network of 8-15-1 was constructed. The sample size used to train the neural network should be neither too large nor too small. [PERSON] [35] conducted a special study on the number of training samples and proposed the 'rule of thumb'. [PERSON] and [PERSON] [36] provided a more specific range of the number of training samples \(\alpha\); that is, \(\mathrm{W}/\gamma<\alpha<(\mathrm{W}/\gamma)\mathrm{log}(\mathrm{N}/\gamma)\), where N is the number of neurons and \(\mathrm{W}\) is the number of ownership values. Therefore, the appropriate sample size for this study was in the range of 1350-3213. Figure 11: Statistical analysis results of laying method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Index Factors**} & \multicolumn{1}{c}{**Index Classification**} & \multirow{2}{*}{\(W_{\beta}\)} & \multirow{2}{*}{**Index Factors**} & \multicolumn{1}{c}{**Index Classification**} & \multirow{2}{*}{\(W_{\beta}\)} \\ \hline \multirow{4}{*}{Elevation (m)} & 37–500 & \(-\)0.345 & & Mudstone & 0.332 \\ \cline{2-6}  & 500–1000 & 1.092 & & Silty mudstone & \(-\)1.815 \\ \cline{2-6}  & 1000–1500 & \(-\)1.46 & \multirow{2}{*}{Stratum lithology} & Argillaceous rock & 0.047 \\ \cline{2-6}  & 1500–1974 & 0.246 & & Carbonate rock & \(-\)0.211 \\ \hline \multirow{4}{*}{Slope (\({}^{\circ}\))} & 0–10 & \(-\)0.732 & & Quaternary accumulation soil & \(-\)1.282 \\ \cline{2-6}  & 10–20 & 0.251 & & 0–43 & 1.379 \\ \cline{2-6}  & 20–30 & 0.514 & & 43–85 & \(-\)0.023 \\ \cline{2-6}  & 30–40 & \(-\)0.111 & & 85–128 & \(-\)0.204 \\ \cline{2-6}  & 40–83 & 0.238 & \multirow{2}{*}{NDVI} & 128–170 & \(-\)0.038 \\ \hline \multirow{4}{*}{Aspect (\({}^{\circ}\))} & 0–45 & \(-\)0.511 & & 170–210 & \(-\)1.023 \\ \cline{2-6}  & 45–90 & \(-\)0.149 & & 210–250 & \(-\)6.249 \\ \cline{2-6}  & 90–135 & \(-\)0.087 & & 0–3375 & \(-\)3.103 \\ \cline{2-6}  & 135–180 & 0.808 & & 3375–6750 & \(-\)0.199 \\ \cline{2-6}  & 180–225 & \(-\)0.077 & & 6750–10,125 & \(-\)0.262 \\ \cline{2-6}  & 225–270 & 0.573 & \multirow{2}{*}{Pipe laying method} & 10,125–13,500 & \(-\)0.875 \\ \cline{2-6}  & 270–315 & \(-\)0.328 & & 13,500–16,875 & 1.561 \\ \cline{2-6}  & 315–360 & \(-\)0.828 & & 16,875–20,250 & 0.588 \\ \hline \multirow{4}{*}{Section curvature} & \textgreater{}0 & \textgreater{}0.898 & & 20,250–23,625 & 1.692 \\ \cline{2-6}  & \textless{}0 & 0.898 & & 23,625–27,000 & 0.541 \\ \hline \multirow{4}{*}{Rainfall (m)} & 1000–1200 & \(-\)0.199 & & & \\ \cline{2-6}  & 1200–1600 & 0.0716 & & & \\ \cline{1-1} \cline{2-6}  & \textgreater{}2000 & \(-\)0.754 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Index factors and \(W_{\text{fi}}\). Figure 12: Hazard zoning map. In this study, we used 273,320,894 grid units and 392,572 landslide grid units in the area along the pipeline. A total of 1500 random landslide grids and non-landslide grids were selected, and a total of 3000 grids were used as training samples. Index factor attributed values in the grid were extracted as input-layer data, and risk index as output-layer data.