Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine
It is significant to do landslide susceptibility assessment (LSA) accurately and efficiently using an appropriate model for landslide prediction and prevention. This article aims to compare the frequency ratio (FR) model with the support vector machine (SVM), for mapping the landslide susceptibility...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-01-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19475705.2018.1482963 |
| _version_ | 1818310367419301888 |
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| author | Faming Huang Chi Yao Weiping Liu Yijing Li Xiaowen Liu |
| author_facet | Faming Huang Chi Yao Weiping Liu Yijing Li Xiaowen Liu |
| author_sort | Faming Huang |
| collection | DOAJ |
| description | It is significant to do landslide susceptibility assessment (LSA) accurately and efficiently using an appropriate model for landslide prediction and prevention. This article aims to compare the frequency ratio (FR) model with the support vector machine (SVM), for mapping the landslide susceptibility of Nantian area in southeastern hilly area, China. To begin, 70 recorded landslides are identified through field investigation and the land and recourse department, 50% of the landslide grid cells are used to train the models and the remaining 50% of the landslide grid cells are used to test the models. Ten environmental factors are used in the modeling of LSA, including the elevation, slope, aspect, plan curvature, profile curvature, relief amplitude, lithology factor, distance to river, Normalized Difference Build-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Then the landslide susceptibility maps of Nantian area are produced by the FR and SVM models, respectively. Finally, the accuracies and efficiencies of both two models are evaluated and compared. The results show that the landslide susceptibility distribution characteristics of Nantian area are explored well by the two models, and the FR model has higher prediction rate and is considerably more efficient than SVM for LSA. |
| first_indexed | 2024-12-13T07:44:56Z |
| format | Article |
| id | doaj.art-17c0b4b55244425ebd1b7a5e89cb9425 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-12-13T07:44:56Z |
| publishDate | 2018-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-17c0b4b55244425ebd1b7a5e89cb94252022-12-21T23:54:52ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132018-01-019191993810.1080/19475705.2018.14829631482963Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machineFaming Huang0Chi Yao1Weiping Liu2Yijing Li3Xiaowen Liu4Nanchang UniversityNanchang UniversityNanchang UniversityNanchang UniversityNanchang UniversityIt is significant to do landslide susceptibility assessment (LSA) accurately and efficiently using an appropriate model for landslide prediction and prevention. This article aims to compare the frequency ratio (FR) model with the support vector machine (SVM), for mapping the landslide susceptibility of Nantian area in southeastern hilly area, China. To begin, 70 recorded landslides are identified through field investigation and the land and recourse department, 50% of the landslide grid cells are used to train the models and the remaining 50% of the landslide grid cells are used to test the models. Ten environmental factors are used in the modeling of LSA, including the elevation, slope, aspect, plan curvature, profile curvature, relief amplitude, lithology factor, distance to river, Normalized Difference Build-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Then the landslide susceptibility maps of Nantian area are produced by the FR and SVM models, respectively. Finally, the accuracies and efficiencies of both two models are evaluated and compared. The results show that the landslide susceptibility distribution characteristics of Nantian area are explored well by the two models, and the FR model has higher prediction rate and is considerably more efficient than SVM for LSA.http://dx.doi.org/10.1080/19475705.2018.1482963Landslide susceptibility assessmentenvironmental factorsfrequency ratio modelsupport vector machine |
| spellingShingle | Faming Huang Chi Yao Weiping Liu Yijing Li Xiaowen Liu Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine Geomatics, Natural Hazards & Risk Landslide susceptibility assessment environmental factors frequency ratio model support vector machine |
| title | Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine |
| title_full | Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine |
| title_fullStr | Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine |
| title_full_unstemmed | Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine |
| title_short | Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine |
| title_sort | landslide susceptibility assessment in the nantian area of china a comparison of frequency ratio model and support vector machine |
| topic | Landslide susceptibility assessment environmental factors frequency ratio model support vector machine |
| url | http://dx.doi.org/10.1080/19475705.2018.1482963 |
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