A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network

详细信息    查看全文

• 作者：Liang-Jie Wang ; Min Guo ; Kazuhide Sawada ; Jie Lin ; Jinchi Zhang
• 关键词：landslide susceptibility mapping ; logistic regression (LR) ; frequency ratio (FR) ; decision tree (DT) ; weights of evidence (WOE) ; artificial neural network (ANN)
• 刊名：Geosciences Journal
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：20
• 期：1
• 页码：117-136
• 全文大小：1,596 KB
• 参考文献：Ayalew, L. and Yamagishi, H., 2005, The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakudayahiko Mountains, Central Japan. Geomorphology, 65, 15–31.CrossRef
  Althuwaynee, O.F., Pradhan, B., Park, H.J., and Lee, J.H., 2014, A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Cantena, 114, 21–36. doi:10.1016/j.catena.2013.10.011
  Bai, S.B., Wang, J., Guo, N.L., Zhou, P.G., Hou, S.S., and Xu, S.N., 2010, GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorgesarea, China. Geomorphology, 115, 23–31.CrossRef
  Bui, D.T., Pradhan, B., Lorfman, O., Revhaug, I., and Dick, O.B, 2012, Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology, 171–172, 12–29. doi:10.1016/j.geomorph.2012.04.023
  Conforti, M., Pascale, S., Robustelli, G., and Sdao, F., 2014, Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Cantena, 113, 236–250. doi:10.1016/j.catena.2013.08.006
  Das, I., Sahoo, S., Van Westen, C., Stein, A., and Hack, R., 2010, Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology, 114, 627–637.CrossRef
  Fawcett, T., 2006, An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874CrossRef
  Felicisimo, A., Cuartero, A., Remondo, J., and Quiros, E., 2013, Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides, 10, 175–189.CrossRef
  Mittal, S.K., Singh, M., Kapur, P., Sharma, B.K., and Shamshi, M.A., 2008, Design and development of instrument network for landslide monitoring, an issue an early warning. Journal of Scientific & Industrial research, 67, 361–365.
  Nandi, A. and Shakoor, A., 2010, A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110, 11–20.CrossRef
  Nefeslioglu, H.A., Gokceoglu, C., and Sonmez, H., 2008, An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97, 171–191.CrossRef
  Neuhauser, B., Damm, B., and Terhorst, B., 2011, GIS-based assessment of landslide susceptibility on the base of the weights of evidence model. Landslides, 9, 511–528.CrossRef
  Nourani, V., Pradhan, B., Ghaffari, H., and Sharifi, S.S., 2014, Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Natural Hazards, 71, 523–547.CrossRef
  Lee, S. and Talib, J.A., 2005, Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990.CrossRef
  Oh, H.J., Lee, S., and Soedradjat, G., 2010, Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. Environmental Earth Sciences, 60, 1317–1328.CrossRef
  Ozdemir, A. and Altural, T., 2013, A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180–197.CrossRef
  Poudyal, C.P., Chang, C., Oh, H.J., and Lee, S., 2010, Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environmental Earth Sciences, 61, 1049–1064.CrossRef
  Pradhan, B., 2010a, Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, 38, 301–320.CrossRef
  Pradhan, B., 2010b, Remote sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research, 45, 1244–1256.CrossRef
  Pradhan, B., 2013, A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350–365.CrossRef
  Pradhan, B. and Lee, S., 2010a, Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60, 1037–1054.CrossRef
  Pradhan, B. and Lee, S., 2010b, Landslide susceptibility assessment and factor effect analysis: back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25, 747–759.CrossRef
  Pradhan, B., Lee, S., and Buchroithner, M.F., 2010, A GIS-based back-propagation neural network model and its cross application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems, 34, 216–235.CrossRef
  Regmi, N.R., Giardino, J.R., and Vitek, J.D., 2010a, Assessing susceptibility to landslide: Using models to understand observed changes in slopes. Geomorphology, 122, 25–38.CrossRef
  Regmi, N.R., Giardino, J.R., and Vitek, J.D., 2010b, Modeling susceptibility to landslides using the weight of evidence approach: western Colorado, USA. Geomorphology, 115, 172–187.CrossRef
  Saito, H., Nakayama, D., and Matsuyama, H., 2009, Comparison of landslide susceptibility based on a decision tree model and actual landslide occurrence: The Akaishi Mountains, Japan. Geomorphology, 109, 108–121.CrossRef
  Shahabi, H., Hhezri, S., Ahmhad, B.B., and Hashim, M., 2014, Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena, 115, 55–70.CrossRef
  Vahidnia, M.H., Alesheikh, A.A., Alimohammadi, A., and Hosseinali, F., 2010, A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Computers & Geosciences, 36, 1101–1114.CrossRef
  Van Westen, C.J., Rengers, N., and Soeters, R., 2003, Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards, 30, 399–419.CrossRef
  Varnes, D.J., 1978, Slope movements: types and processes. In: Schuster, R.L. and Krizek, R.J. (eds.), Landslide analysis and control. National Academy of Sciences, Transportation Research Board Special Report 176, Washington, 11–33.
  Wang, L.J., Sawada, K., and Moriguchi, S., 2013, Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences, 57, 81–92.CrossRef
  Xu, C., Xu, X., Dai, F., and Saraf, A., 2012, Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers & Geosciences, 46, 317–329.CrossRef
  Yalcin, A., 2008, GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistic in Ardesen (Turkey): comparisons of result and confirmations. Catena, 72, 1–12.CrossRef
  Yalcin, A., Reis, S., Aydinoglu, A.C., and Yomralioglu, T., 2011, A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena, 85, 274–287.CrossRef
  Yeon, Y.-K., Han, J.-G., and Ryu, K.H., 2010, Landslide susceptibility mapping in Injae, Korea, using a decision tree. Engineering Geology, 116, 274–283.CrossRef
  Yesilnacar, E. and Topal, T., 2005, Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology, 79, 251–266.CrossRef
  Yilmaz, I., 2009, Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Computers & Geosciences, 35, 1125–1138.CrossRef
  Yilmaz, I., 2010, Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61, 821–836.CrossRef
  
• 作者单位：Liang-Jie Wang (1) (2)
  Min Guo (3)
  Kazuhide Sawada (3)
  Jie Lin (2)
  Jinchi Zhang (2)
  
  1. Jiangsu Provincial Key Laboratory of Soil Erosion and Ecological Restoration, Nanjing Forestry University, Nanjing, 210037, China
  2. College of Forestry, Nanjing Forestry University, Nanjing, 210037, China
  3. Graduate School of Engineering, Gifu University, Gifu, 5011193, Japan
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Geosciences
  
• 出版者：The Geological Society of Korea, co-published with Springer
• ISSN：1598-7477

文摘

For the purpose of comparing susceptibility mapping methods in Mizunami City, Japan, the landslide inventory was partitioned into three groups as various training and test datasets to identify the most appropriate method for creating a landslide susceptibility map. A total of fifteen landslide susceptibility maps were produced using frequency ratio, logistic regression, decision tree, weights of evidence and artificial neural network models, and the results were assessed using existing test landside points and areas under the relative operative characteristic curve (AUC). The validation results indicated that the logistic regression model could provide the highest AUC value (0.865), and a relatively high percentage of landslide points fell in the high and very high landslide susceptibility classes in this study. Furthermore, the paper also suggested that the model performances would be increased if appropriate landslide points were used for the calculation. Key words landslide susceptibility mapping logistic regression (LR) frequency ratio (FR) decision tree (DT) weights of evidence (WOE) artificial neural network (ANN)