Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China

• Main Authors: Wei Chen, Hongxing Han, Bin Huang, Qile Huang, Xudong Fu
• Format: Article
• Language: English
• Published: MDPI AG 2017-11-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: geographic information system (GIS); landslide; susceptibility; K-means clustering; binarization; analytic hierarchy process (AHP); variable-weighted linear combination; China
• Online Access: https://www.mdpi.com/2220-9964/6/11/347

A landslide susceptibility map plays an essential role in urban and rural planning. The main purpose of this study is to establish a variable-weighted linear combination model (VWLC) and assess its potential for landslide susceptibility mapping. Firstly, different objective methods are employed for data processing rather than the frequently-used subjective judgments: K-means clustering is used for classification; binarization is introduced to determine buffer length thresholds for locational elements (road, river, and fault); landslide area density is adopted as the contribution index; and a correlation analysis is conducted for suitable factor selection. Secondly, considering the dimension changes of the preference matrix varying with the different locations of the mapping cells, the variable weights of each optimal factor are determined based on the improved analytic hierarchy process (AHP). On this basis, the VWLC model is established and applied to regional landslide susceptibility mapping for the Shennongjia Forestry District, China, where shallow landslides frequently occur. The obtained map is then compared with a map using the traditional WLC, and the results of the comparison show that VWLC is more reasonable, with a higher accuracy, and can be used anywhere that has the same or similar geological and topographical conditions.