## 4 Results and Discussions
### Landslide Susceptibility Mapping  
The study utilizes two models, BPNN and RF, for training, testing, and landslide susceptibility prediction. Both sets of negative samples consist of 1000 non-landslide points selected using random sampling. These non-landslide points are evenly distributed within the sampling area. Based on the environmental factor analysis and feature selection in Section 3.2, the frequency ratio values of ten environmental factors, including elevation, slope, aspect, land cover, distance from road, etc., are used as model input variables,  
Figure 6: Sampling distribution. with landslide points (labeled as 1) and non-landslide points (labeled as 0) serving as output variables. Based on the Python programming environment and the Scikit-learn library, I-RF, I-BPNN, II-RF, and II-BPNN models are constructed. The 834 assigned landslide grid cells and 1000 non-landslide grid cells are divided into training and testing datasets. The training dataset is used to train the landslide models, while the testing dataset is used to validate the performance of the landslide models [34], with 70% allocated to the training dataset and 30% to the testing dataset. The influence factor layers with 30 m resolution are used as input data and inputted into the trained and tested models, resulting in the probability distribution map of landslide susceptibility index with a resolution of 30 \(\times\) 30 m (Figure 7). Compared to the I-RF and I-BPNN models, the II-RF and II-BPNN models exhibit a more distinct polarization in the distribution of landslide susceptibility index, where blue and red areas are more prominent in the figure. Due to the classification characteristics of the RF model, the key features play a crucial role in influencing the final probability distribution of susceptibility index, which is evident in the figures of I-RF and II-RF. It is worth noting that, compared to the two RF models, the analysis results of the two BPNN models show fewer fragmented effects. This reflects the advantage of BPNN models in handling complex, nonlinear classification problems. Using the natural breaks method, the landslide susceptibility index is divided into five susceptibility levels: very low, low, medium, high, and very high. A landslide susceptibility map (Figure 8) is generated, allowing for a clear observation of the distribution of different levels, reflecting the differences in geological risk distribution within the study area. Dur  
Figure 7: Landslide susceptibility index map. ing the statistical and computational processes, detailed data on the area proportion and landslide proportion of each level are obtained, with special attention to the impact of the sampling partition and optimization of negative samples. Furthermore, the _FR_ values of each level are calculated to measure the influence of negative sample selection on model performance. Comparing the area distribution of susceptibility levels among the four models, II-BPNN and II-RF models exhibit a polarization in area proportion compared to I-RF and I-BPNN models. Specifically, the proportions of very low and very high levels predicted by I-RF, I-BPNN, II-RF, and II-BPNN models are 37.71%, 40.29%, 60.6%, and 76.42%, respectively, with proportions of low, medium, high, and susceptibility gradually decreasing. Regarding the prediction results, the proportions of very high and high susceptibility areas predicted by I-RF, I-BPNN, II-RF, and II-BPNN models are 26.4%, 31.98%, 38.98%, and 40.43%, respectively. As the prediction of very high and high susceptibility regions reflects the accuracy of susceptibility assessment, it further highlights the crucial role of negative sample selection in landslide susceptibility evaluation. Precise selection of negative samples helps the model better understand and capture the characteristics of high-risk areas, thereby improving the overall performance and prediction accuracy of the model. The frequency ratio analysis in Section 3.2 reveals several key characteristics of landslide-prone areas in western Henan.