## 4 Results and Discussions
### Accuracy Analysis  
Receiver Operating Characteristic (ROC) curve, a commonly used tool in machine learning, is an effective method for assessing classifier performance. This curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at different thresholds, depicting the model's performance at various operating points. An ROC curve closer to the upper-left corner indicates better classifier performance. The Area Under the Curve (AUC) is a crucial metric in ROC analysis, representing the classifier's ability to distinguish between positive and negative samples. In this study, AUC (ranging from 0 to 1), Accuracy, Specificity, and Recall are used to evaluate the overall performance of the constructed models. Accuracy represents the proportion of correctly classified samples out of the total samples; Recall is the probability of correctly predicting landslide samples; and Specificity is the probability of correctly predicting non-landslide samples. The precision evaluation results, as shown in Figure 9 and Table 3, display the ROC curves for the I-RF and I-BPNN models on the left and the curves for the II-RF and II-BPNN models on the right. From the figures, the AUC values for the I-RF, I-BPNN, II-RF, and II-BPNN models are 0.8247, 0.8068, 0.9464, and 0.9522, respectively. The model accuracy \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Susceptibility** & **Class** & **\%Class** & **Landslide** & **\%Landslide** & **FR** \\  & **Level** & **Pixels** & **Pixels** & **Pixels** & **Pixels** & **Pixels** \\ \hline \multirow{4}{*}{I-RF} & Very Low & 8,757,965 & 26.97\% & 6 & 0.72\% & 0.027 \\  & Low & 8,696,789 & 26.78\% & 24 & 2.88\% & 0.107 \\  & Medium & 6,442,641 & 19.84\% & 53 & 6.35\% & 0.320 \\  & High & 5,086,075 & 15.66\% & 127 & 15.23\% & 0.972 \\  & Very High & 3,487,518 & 10.74\% & 624 & 74.82\% & 6.966 \\ \hline \multirow{4}{*}{I-BPNN} & Very Low & 7,905,889 & 24.35\% & 25 & 3.00\% & 0.123 \\  & Low & 8,143,737 & 25.08\% & 71 & 8.51\% & 0.339 \\  & Medium & 6,037,252 & 18.59\% & 94 & 11.27\% & 0.606 \\  & High & 5,208,466 & 16.04\% & 194 & 23.26\% & 1.450 \\  & Very High & 5,175,644 & 15.94\% & 450 & 53.96\% & 3.385 \\ \hline \multirow{4}{*}{II-RF} & Very Low & 11,034,546 & 33.98\% & 13 & 1.56\% & 0.046 \\  & Low & 5,575,632 & 17.17\% & 16 & 1.92\% & 0.112 \\ \cline{1-1}  & Medium & 3,204,537 & 9.87\% & 33 & 3.96\% & 0.401 \\ \cline{1-1}  & High & 4,012,344 & 12.36\% & 105 & 12.59\% & 1.019 \\ \cline{1-1}  & Very High & 8,643,929 & 26.62\% & 667 & 79.98\% & 3.004 \\ \hline \multirow{4}{*}{II-BPNN} & Very Low & 14,106,961 & 43.44\% & 56 & 6.71\% & 0.155 \\ \cline{1-1}  & Low & 3,051,532 & 9.40\% & 39 & 4.68\% & 0.498 \\ \cline{1-1}  & Medium & 2,185,191 & 6.73\% & 35 & 4.20\% & 0.624 \\ \cline{1-1}  & High & 2,418,843 & 7.45\% & 58 & 6.95\% & 0.934 \\ \cline{1-1}  & Very High & 10,708,461 & 32.98\% & 646 & 77.46\% & 2.349 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results of landslide susceptibility evaluation grades for machine learning models. ranking is as follows: II-BPNN (AUC = 0.9522) > II-RF (AUC = 0.9464) > I-RF (AUC = 0.8247) > I-BPNN (AUC = 0.8068). This indicates a significant improvement in model accuracy by adjusting the selection of negative samples without changing the positive samples or model parameters. The original unoptimized negative samples have a noticeable impact on the predictive performance of the models. Compared to the I-RF model, the II-RF model shows improvements of 12.17% in AUC, 15.61% in Accuracy, 15.80% in Specificity, and 15.09% in Recall. Similarly, the II-BPNN model outperforms the I-BPNN model by 14.54% in AUC, 16.52% in Accuracy, 14.99% in Specificity, and 17.47% in Recall. The Recall and Specificity of the II-RF model are 94.82% and 86.78%, respectively, while those of the II-BPNN model are 95.50% and 86.36%, respectively, indicating better predictive performance in identifying landslide and non-landslide areas.