Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine

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• 作者：Cheng Su ; Lili Wang ; Xizhi Wang ; Zhicai Huang ; Xiaocan Zhang
• 关键词：Landslide susceptibility ; Rainfall ; Support vector machine ; Receiver operation characteristic ; GIS ; China
• 刊名：Natural Hazards
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：76
• 期：3
• 页码：1759-1779
• 全文大小：8,524 KB
• 参考文献：1. Akgun, A, Dag, S, Bulut, F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54: pp. 1127-1143 CrossRef
  2. Akgun, A, Sezer, EA, Nefeslioglu, HA, Gokceoglu, C, Pradhan, B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38: pp. 23-34 CrossRef
  3. Aleotti, P, Chowdhury, R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58: pp. 21-44 CrossRef
  4. Althuwaynee, OF, Pradhan, B, Lee, S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44: pp. 120-135 CrossRef
  5. Althuwaynee, OF, Pradhan, B, Park, H-J, Lee, JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114: pp. 21-36 CrossRef
  6. Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014b) A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides 11:1063-078. doi:10.1007/s10346-014-0466-0
  7. Ayalew, L, Yamagishi, H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda–Yahiko Mountains, Central Japan. Geomorphology 65: pp. 15-31 CrossRef
  8. Baeza, C, Corominas, J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Proc Land 26: pp. 1251-1263 CrossRef
  9. Bai, S-B, Wang, J, Lü, G-N, Zhou, P-G, Hou, S-S, Xu, S-N (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three Gorges area, China. Geomorphology 115: pp. 23-31 CrossRef
  10. Ballabio, C, Sterlacchini, S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Math Geosci 44: pp. 47-70 CrossRef
  11. Brenning, A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Sys 5: pp. 853-862 CrossRef
  12. Carrara, A, Cardinali, M, Detti, R, Guzzetti, F, Pasqui, V, Reichenbach, P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16: pp. 427-445 CrossRef
  13. Carrara, A, Cardinali, M, Guzzetti, F, Reichenbach, P GIS technology in mapping landslide hazard. In: Carrara, A, Guzzetti, F eds. (1995) Geographical information systems in assessing natural hazards. Springer, Netherlands, pp. 135-175 CrossRef
  14. Carrara, A, Crosta, G, Frattini, P (2003) Geomorphological and historical data in assessing landslide hazard. Earth Surf Proc Land 28: pp. 1125-1142 CrossRef
  15. Chacón, J, Irigaray, C, Fernández, T, Hamdouni, R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65: pp. 341-411 CrossRef
  16. Chang, K-T, Chiang, S-H (2009) An integrated model for predicting ra
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geophysics and Geodesy
  Geotechnical Engineering
  Civil Engineering
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-0840

文摘

This study presents a framework for mapping rainfall-induced landslide susceptibility in the Wencheng area of Zhejiang Province, China, using support vector machine (SVM). Seven conditioning factors of elevation, slope, aspect, distance to roads, lithology, land use, and normalized difference vegetation index selected by correlation analyses, and two triggering factors of daily and cumulative rainfall data were employed as input data in the SVM modeling. The training dataset was constructed using 354 landslide inventories identified from field surveys between 1981 and 2010, and 354 points not prone to slides selected as positive and negative samples, respectively, based on a statistical method of factors. A fivefold cross-validation and receiver operation characteristic were applied to evaluate the performances of the SVM model. The summarized area under the curve result was 0.96, indicating that SVM demonstrated good and stable performance in mapping landslide susceptibility. Two practical cases were investigated: the Trami typhoon of August 22, 2013, and the Kong-Re typhoon of August 26, 2013, which brought heavy rainfall of 300 and 111?mm, respectively, to the Wencheng area. The resulting maps showed that SVM could provide a map with high probability values over small areas, demonstrating good properties of generalization. In addition, mapping of landslide susceptibility could be converted easily into landslide warning by replacing the triggering factor with weather forecasting data. Landslide susceptibility maps based on the results of this study could be used to assist governments and planners, and help reduce the economic and social costs of rainfall-induced landslides.