Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China

Abstract

At present, landslide susceptibility assessment (LSA) based on the characteristics of landslides in different areas is an effective prevention measure for landslide management. In Enshi County, China, the landslides are mainly triggered by high-intensity rainfall, which causes a large number of casualties and economic losses every year. In order to effectively control the landslide occurrence in Enshi County and mitigate the damages caused by the landslide. In this study, eight indicators were selected as assessment indicators for LSA in Enshi County. The analytic hierarchy process (AHP) model, information value (IV) model and analytic hierarchy process-information value (AHP-IV) model were, respectively, applied to assess the landslide distribution of landslides in the rainy season (RS) and non-rainy season (NRS). Based on the three models, the study area was classified into five levels of landslide susceptibility, including very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. The receiver operating characteristic (ROC) curve was applied to verify the model accuracy. The results showed that the AHP-IV model (ROC = 0.7716) was more suitable in RS, and the IV model (ROC = 0.8237) was the most appropriate model in NRS. Finally, combined with the results of landslide susceptibility in RS and NRS, an integrated landslide susceptibility map was proposed, involving year-round high susceptibility, RS high susceptibility, NRS high susceptibility and year-round low susceptibility. The integrated landslide susceptibility results provide a more detailed division in terms of the different time periods in a year, which is beneficial for the government to efficiently allocate landslide management funds and propose effective landslide management strategies. Additionally, the focused arrangement of monitoring works in landslide-prone areas enable collect landslide information efficiently, which is helpful for the subsequent landslide preventive management.

Regional Landslide Susceptibility Assessment and Model Adaptability Research

“…Table 1 presents statistics on landslide disasters in Xupu County townships. With distinct seasonal rainfall patterns, concentrated early summer rainfall, significant terrain relief, and numerous fault structures, Xupu County has multiple factors triggering landslides, making it suitable as the study area for this research [14][15][16].…”

Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model

“…However, this subjective experience leads to the uncertainty of the model [15]. Mathematical statistical models [16] including the information value method (IV) [17] and deterministic coefficient method, among others, rely on the engineering analogy method and superimpose factors in different ways to express the nonlinear relationship between factors and landslides. Machine learning models such as logistic regression [18], SVM [19], and RF [16] can efficiently capture the relationship between factors and landslides, which are widely used in landslide susceptibility assessment on account of their excellent performance and efficient modeling process [20], although partial models mat be challenging to interpret due to the black-box analysis process.…”