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Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine
AbstractAmong 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.
HighlightsReasonable non-landslides are selected from the very low susceptible area produced by self-organizing-map (SOM) network.SOM-extreme learning machine (ELM) is successfully used to map landslide susceptibility in Wanzhou district.SOM-ELM possesses higher accuracy than single ELM and SOM-support vector machine (SVM) models.ELM has higher prediction efficiency than SVM for susceptibility indexes calculation.
Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine
AbstractAmong 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.
HighlightsReasonable non-landslides are selected from the very low susceptible area produced by self-organizing-map (SOM) network.SOM-extreme learning machine (ELM) is successfully used to map landslide susceptibility in Wanzhou district.SOM-ELM possesses higher accuracy than single ELM and SOM-support vector machine (SVM) models.ELM has higher prediction efficiency than SVM for susceptibility indexes calculation.
Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine
Huang, Faming (author) / Yin, Kunlong (author) / Huang, Jinsong (author) / Gui, Lei (author) / Wang, Peng (author)
Engineering Geology ; 223 ; 11-22
2017-04-16
12 pages
Article (Journal)
Electronic Resource
English
Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine
| British Library Online Contents | 2017
| Taylor & Francis Verlag | 2023