Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model - Yonghong Xiao; Guolong Li; Lu Wei; Jing Ding; Zhen Zhang (Applied Sciences)
As a critical predisaster warning tool, landslide susceptibility assessment is crucial in disaster prevention and mitigation efforts. However, earlier methods for assessing landslide susceptibility have often ignored the impact of similarities in geographical attributes, restricting their feasibility in regions with diverse characteristics. The geographical-optimal-similarity (GOS) model effectively captures similarity relations within geospatial data and can isolate region-specific landslide features, thus overcoming this challenge. Consequently, a landslide susceptibility assessment method was developed by integrating the information value (IV) model with the GOS model. Huangshan City in Anhui Province, China, was selected as the study region. This research used 11 remote sensing feature factors and 657 historical landslide points, combined with the IV model, to construct a dataset for landslide prediction and susceptibility assessment using the GOS model. The findings indicate that, compared to conventional methods such as random forest, logistic regression, and radial basis function classifier, the GOS model enhances the area under the curve (AUC) value by 2.81% to 8.92%, reaching 0.846. This demonstrates superior performance and confirms the effectiveness and accuracy of the method in landslide susceptibility assessment. Furthermore, compared to the basic-configuration-similarity (BCS) model, the GOS model increases the AUC value by 9.64%, achieving 0.846. This approach substantially diminishes the effects of historical data accuracy, revealing upgraded applicability in landslide susceptibility evaluations. Landslides in Huangshan City are primarily influenced by rainfall and vegetation cover. High-susceptibility zones are predominantly located in areas with high precipitation and low vegetation cover. In contrast, low-susceptible and non-susceptible zones are primarily found in flat areas with high vegetation cover and farther from fault lines. The majority of the study region lies within landslide-prone zones, with non-susceptible areas comprising only 12.43% of the total area. Historical landslides are largely concentrated in moderate- to high-susceptibility zones, accounting for 92.24% of all landslide occurrences. Landslide density increases with the susceptibility level, with a density of 0.15 landslides per square kilometre in high-susceptibility zones. This study brings forward a reliable strategy for establishing the spatial relationship between geographical attribute similarity and landslide susceptibility, bolstering the method’s adaptability across various regions.