- Title
- Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine
- Creator
- Huang, Faming; Yin, Kunlong; Huang, Jinsong; Gui, Lei; Wang, Peng
- Relation
- Engineering Geology Vol. 223, p. 11-22
- Publisher Link
- http://dx.doi.org/10.1016/j.enggeo.2017.04.013
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2017
- Description
- Among the machine learning models used for landslide susceptibility indexes calculation, the support vector machine (SVM) is commonly used; however, SVM is time-consuming. In addition, the non-landslide grid cells are selected randomly and/or subjectively, which may result in unreasonable training and validating data for the machine learning models. This study proposes the self-organizing-map (SOM) network-based extreme learning machine (ELM) model to calculate the landslide susceptibility indexes. Wanzhou district in Three Gorges Reservoir Area is selected as the study area. Nine environmental factors are chosen as input variables and 639 investigated landslides are used as recorded landslides. First, an initial landslide susceptibility map is produced using the SOM network, and the reasonable non-landslide grid cells are subsequently selected from the very low susceptible area. Next, the final landslide susceptibility map is produced using the ELM model based on the recorded landslides and reasonable non-landslide grid cells. The single ELM model which selects the non-landslide grid cells randomly, and the SOM network-based SVM model are used for comparisons. It is concluded that the SOM-ELM model possesses higher success and prediction rates than the single ELM and SOM-SVM models, and the ELM has a considerably higher prediction efficiency than the SVM.
- Subject
- landslide susceptibility map; self-organizing-map network; extreme learning machine; support vector machine; three-Gorges Reservoir
- Identifier
- http://hdl.handle.net/1959.13/1349213
- Identifier
- uon:30365
- Identifier
- ISSN:0013-7952
- Language
- eng
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