- Title
- Slope Unit Extraction and Landslide Susceptibility Prediction Using Multi-scale Segmentation Method
- Creator
- Chang, Zhilu; Huang, Faming; Jiang, Shuihua; Zhang, Yinlang; Zhou, Chuangbing; Huang, Jinsong
- Relation
- Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences Vol. 55, Issue 1, p. 184-195
- Relation
- https://jsuese.scu.edu.cn/jsuese_en/ch/reader/create_pdf.aspx?file_no=202200953&flag=1&journal_id=jsuese_en&year_id=2023
- Publisher
- Editorial Department Advanced Engineering Sciences
- Resource Type
- journal article
- Date
- 2023
- Description
- Landslide susceptibility assessment can help us to effectively predict the spatial location of potential landslides, which is the basis of landslide hazard and risk assessment. Slope units are commonly employed to predict landslide susceptibility because they are extracted based on actual landforms and geomorphology with visible geological features. However, one of the key constraints limiting the applicability of slope units and the challenge in current research is how to efficiently and accurately extract slope units and take into account the heterogeneity of conditioning factors within slope units. The Chongyi County was selected as the case study. First, the aspect and shaded relief images were extracted as the initial fundamental data. The multi-scale segmentation (MSS) method was used to extract slope units and the optimal parameter combination including scale, shape weight and compactness weight was determined by combining the trial-and-error method with recorded landslide features. Then, a total of 15 conditioning factors such as elevation, slope and profile curvature were extracted based on slope units and were imported into the support vector machine (SVM) and logistic regression (LR) models to construct Slope–SVM/LR models. Furthermore, the range and standard deviation values were used to represent the heterogeneity of conditioning factors within slope units to construct the Variant Slope–SVM/LR models. Finally, the receiver operating characteristic (ROC) curves and frequency ratio (FR) accuracy were used to evaluate the predicted performance of landslide susceptibility models. The results show that: 1) when the parameters of scale, shape weight and compactness weight were set to 20, 0.8 and 0.8, respectively, slope units extracted by the MSS method in the study area were at their best. 2) The ROC accuracy of the Slope–SVM, Variant slope–SVM, Slope–LR and Variant slope–LR models was 0.812, 0.876, 0.818 and 0.839, respectively. The FR accuracy of those models was 0.780, 0.866, 0.792 and 0.865, respectively, indicating that the predicted accuracy of Variant slope–SVM/LR models was better than that of Slope–SVM/LR models. Therefore, it can be inferred that the MSS method is an effective method to accurately and automatically extract slope units, and the predicted performance of landslide susceptibility models can be significantly improved by considering the heterogeneity of conditioning factors within slope units.
- Subject
- multi-scale segmentation method; slope unit; landslide susceptibility; prediction; heterogeneity
- Identifier
- http://hdl.handle.net/1959.13/1487110
- Identifier
- uon:52061
- Identifier
- ISSN:2096-3246
- Language
- chi
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