Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China)
| Autor: | Wei Chen, Chong Xu, Ahmed M. Youssef, Biswajeet Pradhan, Haoyuan Hong, Dieu Tien Bui |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
landslide
010504 meteorology & atmospheric sciences lcsh:Risk in industry. Risk management Central china 010502 geochemistry & geophysics gis 01 natural sciences support vector machines lcsh:TD1-1066 remote sensing parasitic diseases lcsh:Environmental technology. Sanitary engineering China suichuan lcsh:Environmental sciences 0105 earth and related environmental sciences General Environmental Science Remote sensing lcsh:GE1-350 Landslide Landslide susceptibility lcsh:HD61 Support vector machine Geography Remote sensing (archaeology) General Earth and Planetary Sciences china 0406 Physical Geography and Environmental Geoscience Cartography |
| Zdroj: | Geomatics, Natural Hazards and Risk Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 544-569 (2017) |
| Popis: | Suichuan is a mountainous area at the Jiangxi province in Central China, where rainfall-induced landslides occur frequently. The purpose of this study is to assess landslide susceptibility of this region using support vector machine (SVM) with four kernel functions: polynomial (PL), radial basis function (RBF), sigmoid (SIG), and linear (LN). A total of 178 landslides were used to accomplish this approach, of which, 125 (70%) landslides were randomly selected for training the landslide susceptibility models, whereas the remaining 53 (30%) were used for the model validation. Fifteen landslide conditioning factors were considered including slope-angle, altitude, slope-aspect, topographic wetness index (TWI), sediment transport index (STI), stream power index (SPI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, precipitation, landuse, normalized difference vegetation index (NDVI), and lithology. Using the training dataset, nine landslide susceptibility models for the Suichuan area were constructed with the four kernel functions. To evaluate the performance of these models, the receiver-operating characteristic curve (ROC) and area under the curve (AUC) were used. Using the training dataset, AUC values for the SVM-PL models with six degrees PL function (1–6) are 0.715, 0.801, 0.856, 0.891, 0.919, 0.953, respectively, and for the SVM-RBF model, the SVM-SIG model, and the SVM-LN model are 0.716, 0.741, and 0.740, respectively. Using the validation dataset, AUC values for the SVM-PL models with six degrees PL function (1–6) are 0.738, 0.730, 0.683, 0.648, 0.608, and 0.598, respectively, and for the SVM-RBF model, the SVM-SIG model, and the SVM-LN model are 0.716, 0.741, and 0.740, respectively. Our results suggested that the SVM-RBF model is the most suitable for landslide susceptibility assessment for the study area. |
| Databáze: | OpenAIRE |
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