## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Landslide Susceptibility Mapping and Risk Assessment Based on Weighted Information Entropy
#### 4.4.3 Landslide Risk Zoning Based on the Blending Ensemble Model  
By incorporating the factor of ten-day rainfall, a landslide risk zoning map was generated (Figure 17). The contribution rates of the 16 predisposing factors obtained through the Blending ensemble model were weighted based on information entropy. Ultimately, the landslide risk zoning map of the study area (Figure 18) was obtained. Comparing the risk zoning maps before and after weighting, it was found that in the map based on weighted information entropy, the proportion of extremely high-risk zones in Heshan District (eastern study area) significantly decreased, while some high-risk zones in Anhua County (western study area) were reclassified as medium-risk zones. Overall, the two models exhibited a certain degree of similarity in risk assessment results. In Yiyang City, high and extremely high-risk zones are mainly concentrated in the central-western part of Heshan District, the southern and northern parts of Taojiang County, and the southeastern part and central regions of Anhua County. Specifically, the high-risk zones in central Anhua County extend in an east-west belt-like pattern, while the central-western part of Heshan District and the southern part of Taojiang County collectively form the primary distribution area within Yiyang City. Urban expansion and increasingly frequent human engineering activities have created favorable conditions for landslide formation. The mountainous areas in the southeastern and central parts of Anhua County are also high-risk landslide zones, characterized by densely distributed hills and valleys, steep terrain, and a heightened susceptibility to landslides due to topographic factors. The study indicates that in the weighted information model, a large proportion of areas were classified as high-risk zones, whereas in the single-information model, many regions were categorized as extremely high-risk zones, with the proportion of high-risk zones being much lower than that of extremely high-risk zones. This suggests that the single-information model has relatively weaker performance in identifying landslide-prone areas, lacking clear delineation and specificity in practical applications. During the classification process, it was observed that the landslide point density in the weighted information model was higher than that in the single-information model, particularly in high-risk and extremely high-risk zones, where landslide density increased by 0.036 points/km\({}^{2}\) and 0.048 points/km\({}^{2}\), respectively. The weighted information model demonstrated that landslide points in extremely high-risk zones and high-risk zones were more densely distributed, better aligning with the actual distribution characteristics of landslide occurrences. Therefore, the landslide risk classification based on weighted information entropy exhibits higher rationality and reliability. According to the landslide risk classification based on weighted information entropy, the landslide points in the study area were statistically analyzed by zone, and the results are shown in Table 13. The frequency ratio (FR) reflects the importance of regional classification for landslide occurrence, which is calculated as the ratio of landslide proportion to area proportion. When FR \(\geq\) 1, it indicates that the factors in the zone have a positive contribution to landslide occurrence; if FR < 1 in a low-risk zone, it suggests that the factors in the zone have a negative contribution to landslide occurrence. Evidently, the landslide risk classification in the study area is consistent with the conditions conducive to landslide formation.