- Title
- Uncertainties of Landslide Susceptibility Prediction Modeling: Influence of Different Selection Methods of “Non-landslide Samples”
- Creator
- Huang, Faming; Zeng, Shiyi; Yao, Chi; Xiong, Haowen; Fan, Xuanmei; Huang, Jinsong
- Relation
- Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences Vol. 56, Issue 1, p. 169-182
- Relation
- https://jsuese.ijournals.cn/jsuese_en/ch/reader/view_abstract.aspx?file_no=202201271&flag=1
- Publisher
- Editorial Departmentof Advanced Engineering Sciences
- Resource Type
- journal article
- Date
- 2024
- Description
- How to select non-landslide samples for landslide susceptibility prediction (LSP) modeling is an important uncertainty affecting the LSP results. To study the influence of different non-landslide sample selection methods on LSP modeling, five sampling methods were proposed (Randomly selected from the whole area, from the specific attribute area with a slope lower than 5°, from the area outside buffer zone which is 300 m from each landslide, selected by information value method, selected by Semi-supervised machine learning) with the same number of landslide grid units, and coupled with Random Forest (RF) to construct random selection-RF, low-slope RF, buffer-based RF, IV-RF, and semi-supervised RF models for LSP. Taking Nankang County of Jiangxi province as the study area, a total of 19 environmental factors such as elevation, slope, population density, and road density were acquired, and 233 landslide inventories were obtained. The landslide inventory was divided into 2598 grids as landslide samples to construct the input-output of the above-coupled model. Then, the prediction accuracy and the distribution characteristics of predicted landslide susceptibility indexes were used to analyze the LSP modeling uncertainty. To further solve the problem of unreasonable distribution of landslide susceptibility indexes predicted by the coupled model, a sample set with a 1∶2 ratio of landslide to non-landslide was used for LSP, and the condition of the sample set with equal proportion was compared in semi-supervised RF. Results showed that: 1) The prediction accuracy of models such as low-slope RF, buffer-based RF, IV-RF, and semi-supervised RF was substantially better than that of the random selection-RF model, suggesting that accurate selection of non-landslide samples was critical for LSP. 2) The modeling performance of the semi-supervised RF was optimal, which predicted the distribution characteristics of landslide susceptibility indexes more accurately and reliably at landslide∶non-landslide = 1∶2 than at 1∶1. It is necessary to explore the ratio of landslide to non-landslide samples in depth in future studies.
- Subject
- information value; landslide susceptibility prediction; non-landslide samples selection; random forest; semi-supervised machine learning
- Identifier
- http://hdl.handle.net/1959.13/1499480
- Identifier
- uon:54702
- Identifier
- ISSN:2096-3246
- Language
- Chinese
- Reviewed
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