## 5 Conclusions  
This study concentrates on the western region of Henan Province and develops an index system for evaluating landslide susceptibility based on ten environmental factors: elevation, slope, aspect, profile curvature, land cover, lithology, topographic wetness index, distance from river, distance from faults, and distance from road. Utilizing the susceptibility results from the information value model to define a negative sample sampling area, two sampling methods, frequency ratio and random sampling, are employed. RF and BPNN models are selected to delineate landslide susceptibility zones and compare model accuracy. The main conclusions are as follows:  
Figure 9: ROC curves and AUC values. I-RF, I-BPNN (**Left**); II-RF, II-BPNN (**Right**). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Assessment** & **I-RF** & **I-BPNN** & **II-RF** & **II-BPNN** \\
**Indicators** & & & & \\ \hline AUC & 82.47\% & 80.68\% & 94.64\% & 95.22\% \\ Accuracy & 75.68\% & 74.41\% & 91.29\% & 90.93\% \\ Specificity & 70.98\% & 71.37\% & 86.78\% & 86.36\% \\ Recall & 79.73\% & 77.03\% & 94.82\% & 94.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of machine learning. (1) Analysis using the frequency ratio method reveals that in the study region, areas prone to landslides exhibit characteristics, which include elevations of 400-1000 m; slopes of 10\({}^{\circ}\)-30\({}^{\circ}\); aspects predominantly southeast, south, and southwest; a positive profile curvature; a lithology mainly composed of andesite, andesitic basalt with a small amount of rhyolite and volcanic breccia; a topographic wetness index ranging from 12.5 to 15.0; distances to faults within 1000 m; distances to water bodies within 500 m; land cover consisting mainly of cropland and grassland; and distances to roads within 200 m. The proportions of landslides in the very high and high zones predicted by models I-RF, I-BPNN, II-RF, and II-BPNN are 90.05%, 77.22%, 92.57%, and 84.41%, respectively, and these areas exhibit the aforementioned characteristics, indicating the effective prediction of landslide-prone areas by the four models. (2) The areas of high landslide susceptibility in the western region of Henan Province, predicted by models I-RF, I-BPNN, II-RF, and II-BPNN show an increasing trend, with proportions of high- and very-high-susceptibility areas being 26.4%, 31.98%, 38.98%, and 40.43%, respectively. Compared to models I-RF and I-BPNN, models II-RF and II-BPNN more accurately identify and predict high- and very-high-susceptibility areas. The ROC accuracies of models II-RF and II-BPNN are 0.9464 and 0.9522, respectively, representing an improvement of approximately 12% compared to the single RF model and nearly 15% compared to the single BPNN model. This underscores the importance of sample set selection for model performance. (3) The purpose of optimizing the sampling area for negative samples is to reduce the likelihood of non-landslide samples being located near actual landslide boundaries. This enhances the prediction accuracy and classification ability of the machine learning model, enabling better identification of non-landslide grid cells. In situations where it is difficult to obtain accurate landslide boundaries, this method serves as an alternative approach for achieving relatively accurate landslide susceptibility zoning and prediction results. Conceptualization, X.W. and X.M. ; Data curation, X.M. and D.G. ; Methodology, X.W. and X.M. ; Project administration, Z.H. ; Software, X.M. and D.G. ; Formal analysis, X.M., X.W. and D.G. ; Resources, G.Y. and Z.H. ; Visualization, X.M. and D.G. ; Writing--original draft, X.M. and X.W., Writing--review and editing, X.M., X.W. and G.Y. ; Supervision, G.Y. and Z.H. All authors have read and agreed to the published version of the manuscript. The research leading to these results received funding from the Key Research and Development Project of Henan Province under Grant Agreement No. 221111321500. Not applicable. Not applicable. Not applicable.