Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China

Xu, Chong; Xu, Xiwei; Dai, Fuchu; Saraf, A. K.

doi:10.1016/j.cageo.2012.01.002

Cited by 218 publications

(106 citation statements)

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“…From the validation of the landslide susceptibility maps, the RBF kernel produced AUC values, indicating the accuracy of the landslide susceptibility maps, and these were 81.36% for the PyeongChang area, and 77.49% for the Inje area (Figure 7). There were some differences in accuracy between the study areas, because the previous studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][29][30][31][32][33] showed that the spatial distribution is subject to change, according to the area and event. However, the accuracy was usually high enough, displaying figures of above 80%.…”

Section: Resultsmentioning

confidence: 99%

“…Detailed descriptions of two-class SVM modeling were provided by [2,17,54] and were summarized in the following explanation. Consider a set of linear separable training vectors xi (i = 1, 2, …, n).…”

Section: Methodsmentioning

confidence: 99%

“…The RBF kernel is the default kernel, and it works well in most of the cases (ENVI 4.4 2006). Moreover, in many studies and cases (especially in nonlinear problems), RBF provides better prediction results for landslide susceptibility mapping than other kernels [2,10,57,58]. In order to perform the landslide susceptibility mapping using an SVM, the following steps had to be taken:…”

Section: Methodsmentioning

confidence: 99%

“…In particular, recent case studies have frequently applied soft computing technology to the assessment of landslide hazards. When creating soft computing models, artificial neural networks [2][3][4][5][6], neuro-fuzzy logic [2,[7][8][9], decision trees [10][11][12][13][14][15], and support vector machines (SVMs) [10,[15][16][17][18][19], have been applied in order to analyze landslide landslide susceptibility. Among the many soft computing models, SVMs were applied in the present study.…”

Section: Introductionmentioning

confidence: 99%

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A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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Abstract:In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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“…A detailed and accurate landslide inventory is an essential component of seismic landslide hazard analyses (e.g., Xu et al , 2012dXu et al , 2012e, 2012f, 2013bXu et al , 2013c and spatial distribution statistical analyses (e.g., Xu 2012b, 2013;Dai et al 2011;Gorum et al 2011Gorum et al , 2013Harp et al 2011a). Harp et al (2011a) considered an ideal inventory as one covering the entire area affected by an earthquake, including all the landslides that are detectable down to a size of 1-5 m in length, with the landslides being accurately located and mapped as polygons depicting their true shapes.…”

Section: Mapping Of Landslides Triggered By the Earthquakementioning

confidence: 99%

Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis

et al. 2013

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Spatial Prediction Models for Seismic Landslides Based on Support Vector Machine and Varied Kernel Functions: A Case Study of the 14 April 2010 Yushu Earthquake in China

Xu¹,

Xu²

2012

Chinese J of Geophysics

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It is an important subject to study spatial prediction models of landslides triggered by earthquakes in terms of statistics learning theory and Geological Information Systems (GIS) technology. Such models allow us to predict the area of landslide occurrence during an earthquake under similar conditions in the future. On April 14, 2010 a catastrophic earthquake with Mw 6.9 struck Yushu County, Qinghai Province, China. After this event, a total of 2036 landslides were interpreted from aerial photographs and satellite images, verified by field investigations, distributed in a rectangle area of 1455.3 km 2 . The aim of this study is to carry out spatial prediction modelling of landslides in this rectangle area using several types of kernel functions based on GIS and the support vector machine (SVM) model. A spatial database, including landslides and associated controlling factors which may have influence on the occurrence of landslides, was developed and analyzed using GIS technology. The 12 controlling factors, including elevation, slope angles, slope aspect, slope curvatures, slope positions, drainages, lithology, faults, roads, normalized difference vegetation index (NDVI), co-seismic surface ruptures, and peak ground acceleration (PGA) were selected as the prediction factors. The landslide spatial prediction mapping were produced by using several types of kernel functions such as linear function, polynomial function, radial basis function (RBF), and sigmoid function based on the SVM model. Then landslide hazard index maps, landslide hazard rank maps, and prediction result maps for the Yushu event were created separately. The success rates of the four types of kernel functions are 79.87%, 83.45%, 84.16%, and 64.62%, respectively. The results show that radial basis function (RBF) is the best kernel function among the four kernel functions for spatial prediction mapping of landslides by the Yushu earthquake. The training and discussion of the model were also carried out based on other three different groups of random distribution training samples and one group of regular distribution training samples. This paper provide an example for selecting appropriate types of kernel functions for prediction mapping of seismic landslides using support vector machine modelling. The hazard rank maps and prediction result maps can be useful in efforts of landslide hazard mitigation.

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Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China

Cited by 218 publications

References 45 publications

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis

Spatial Prediction Models for Seismic Landslides Based on Support Vector Machine and Varied Kernel Functions: A Case Study of the 14 April 2010 Yushu Earthquake in China

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