Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy

Computers and Geosciences

Volumn 57, Issue , 2013, Pages 81-92

  Wang, Liang Jie ^a   Sawada, Kazuhide ^a   Moriguchi, Shuji ^a  

^a GIFU UNIVERSITY   (Japan)

Author keywords

FCM; Landslide susceptibility; Logistic regression model

Indexed keywords

FUZZY CLUSTERING; REGRESSION ANALYSIS;

AREA UNDER THE ROC CURVE; CLASSIFICATION ACCURACY; DISTRIBUTION CHARACTERISTICS; FUZZY C-MEANS ALGORITHMS; LANDSLIDE SUSCEPTIBILITY; LANDSLIDE SUSCEPTIBILITY MAPPING; LOGISTIC REGRESSION MODELING; PLANNING AND DEVELOPMENT;

LANDSLIDES;

FUZZY MATHEMATICS; LANDSLIDE; NUMERICAL MODEL; REGRESSION ANALYSIS; SPATIAL DISTRIBUTION;

CHUBU; GIFU; HONSHU; JAPAN;

EID: 84877835260     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2013.04.006     Document Type: Article

References (61)

1. Akgun A., Bulut F. GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environmental Geology 2007, 51:1377-1387.
2. Atkinson P.M., Massari R. Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy. Geomorphology 2011, 10.1016/j.geomorph.2011.02.001.
3. Bai S.B., Wang J., Guo N.L., Zhou P.G., Hou S.S., Xu S.N. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorgesarea, China. Geomorphology 2010, 115:23-31.
4. Bezdek J.C., Ehrlich R., Full W. FCM: the fuzzy c-means clustering algorithm. Computers & Geosciences 1984, 10:191-203.
5. Blyth E.M., Finch J., Robinson M., Rosier P. Can soil moisture be mapped onto the terrain?. Hydrology and Earth System Science 2004, 8:923-930.
6. Brenning A. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences 2005, 5:853-862.
7. Bui D.T., Pradhan B., Lofman O., Revhaug I., Dick O.B. Landslide susceptibility assessment in the Hoa Binh province of Vietnam using Artificial Neural Network. Geomorphology 2012, 171-172:12-29.
8. Chen C.H., Ke C.C., Wang C.L. A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the Southern Cross-Island Highway in Taiwan. Environmental Geology 2009, 57:723-733.
9. Das I., Sahoo S., Van Westen C., Stein A., Hack R. 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 2010, 114:627-637.
10. Dunn J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 1973, 3:32-57.
11. Dipartimento di Elettronica e Informazione, A Tutorial on Clustering Algorithms, http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/cmeans.html.
12. Ercanoglu M., Gokceoglu C. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology 2002, 41:720-730.
13. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006, 27:861-874.
14. Fecher M., Schmidt J.M. Fuzzy clustering as a means of selecting representative conformers and molecular alignments. Journal Chemistry Information Computer Science 2003, 43:810-818.
15. Guzzetti Fausto Landslide hazard and risk assessment. Dissertation 2005.
16. Kanungo D.P., Arora M.K., Gupta R.P., Sarkar S. Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas. Landslides 2008, 5:407-416.
17. Lark R.M. Soil-landform relationships at within-field scales: an investigation using continuous classification. Geoderma 1999, 92:141-165.
18. Lee S. Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surface Processes and Land forms 2007, 32(14):2133-2148.
19. Lee S. Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology 2007, 52:615-623.
20. Lee S., Pradhan B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 2007, 4:33-41.
21. Lee S., Sambath T. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology 2006, 50:847-855.
22. Melchiorre C., Matteucci M., Azzoni A., Zanchi A. Artificial neural net-works and cluster analysis in landslide susceptibility zonation. Geomorphology 2008, 94:379-400.
23. Minasny B., McBratney A.B. FuzME version 3.0, Australian Centre for Precision Agriculture 2002, The University of Sydney, Australia. http://www.usyd.edu.au/su/agric/acpa.
24. Mittal S.K., Singh M., Kapur P., Sharma B.K., Shamshi M.A. Design and development of instrument network for landslide monitoring, an issue an early warning. Journal of Scientific & Industrial research 2008, 67:361-365.
25. Moore.I.D., Grayson R.B., Ladson A.R. Digital terrain modelling- a review of hydrological, geomorphological, and biological applications. Hydrological Process 1991, 5:3-30.
26. Muthu K., Petrou M., Tarantino C., Blonda P. Landslide possibility mapping using fuzzy approaches. IEEE Transactions on Geoscience and Remote Sensing 2008, 46:1253-1265.
27. Nandi A., Shakoor A. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology 2009, 110:11-20.
28. Nefeslioglu H.A., Gokceoglu C., Sonmez H. 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 2008, 97:171-191.
29. Odeh I.O.A., McBratney A.B., Chittleborough D.J. Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Science Society of American Journal 1992, 56:505-516.
30. Oh H.J., Lee S. Cross-validation of logistic regression model for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Advances 2010, 3(2):44-55.
31. Oh H.J., Lee S., Chotikasathien W., Kim C.H., Kwon J.H. Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand. Environmental Geology 2009, 57:641-651.
32. Oh H.J., Pradhan B. Application of a neuro-fuzzy model to landslide susceptibility mapping in a tropical hilly area. Computers & Geosciences 2011, 37:1264-1276.
33. Poudyal C.P., Chang C., Oh H.J., Lee S. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environmental Earth Sciences 2010, 61(5):1049-1064.
34. Pradhan B. 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 2010, 38(2):301-320.
35. Pradhan B. 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 2010, 45:1244-1256.
36. Pradhan B. 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 2013, 51:350-365.
37. Pradhan B., Buchroithner M.F. Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environmental and Engineering Geoscience 2010, 16:107-126.
38. Pradhan B., Lee S. Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis by using an artificial neural network model. Earth Science Frontier 2007, 14(6):143-152.
39. Pradhan B., Lee S. Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal of Physical Sciences 2009, 4(1):1-15.
40. Pradhan B., Lee S. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences 2010, 60:1037-1054.
41. Pradhan B., Lee S. 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 2010, 25(6):747-759.
42. Pradhan B., Lee S. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 2010, 7:13-30.
43. Pradhan B., Lee S., Buchroithner M.F. A GIS-based back-propagation neural network model and its cross application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems 2010, 34:216-235.
44. Regmi N.R., Giardino J.R., Vitek J.D. Modeling susceptibility to landslides using the weight of evidence approach: western Colorado, USA. Geomorphology 2010, 115:172-187.
45. Regmi N.R., Giardino J.R., Vitek J.D. Assessing susceptibility to landslide: using models to understand observed changes in slopes. Geomorphology 2010, 122(4):25-38.
46. Saito H., Nakayama D., Matsuyama H. Comparison of landslide susceptibility based on a decision tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology 2009, 109:108-121.
47. Sezer E., Pradhan B., Gokceoglu C. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley. Malaysia. Expert Systems with Applications 2011, 38:8208-8219.
48. Sterlacchini S., Ballabio C., Blahut J., Masetti M., Sorichetta A. Spatial agreement of predicted patterns in landslide susceptibility maps. Geomorphology 2010, 10.1016/j.geomorph.2010.09.004.
49. Sun.X.-L., et al. Sensitivity of digital soil maps based on FCM to the fuzzy exponent and the number of clusters. Geoderma 2012, 171-172:24-34.
50. Süzen M.L., Doyuran V. Data driven bivariate landslide susceptibility assessment using Geographical Information Systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology 2004, 71:303-321.
51. Tunusluoglu M.C., Gokceoglu C., Nefeslioglu H.A., Sonmez H. Extraction of potential debris source areas by logistic regression technique: a case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey). Environmental Geology 2008, 54(1):9-22.
52. Vahidnia M.H., Alesheikh A.A., Alimohammadi A., Hosseinali F. A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Computers & Geosciences 2010, 36(9):1101-1114.
53. 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.
54. Xie X.L., Beni G. A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991, 13:841-847.
55. Xu C., Xu X., Dai F., Saraf A. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers & Geosciences 2012, 46:317-329.
56. Yao X., Tham L.G., Dai F.C. Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 2008, 101:572-582.
57. Yalcin A., Reis S., Aydinoglu A.C., Yomralioglu T. 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 2011, 85:274-287.
58. Yeon Y.-K., et al. Landslide susceptibility mapping in Injae, Korea, using a decision tree. Engineering Geology 2010, 10.1016/j.enggeo.2010.09.009.
59. Yesilnacar E., Topal T. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology 2005, 79:251-266.
60. Yilmaz I. Comparison of landslide susceptibility mapping methodologies for Koyulhisar. Environmental Earth Sciences 2010, 61(4):821-836.
61. Zarea M., Pourghasemi H.R., Vafakhah M., Pradhan B. Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multi-layer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences 2012, 10.1007/s12517-012-0610-x.