Discerning landslide susceptibility using rough sets

Computers, Environment and Urban Systems

Volumn 32, Issue 1, 2008, Pages 53-65

  Gorsevski, Pece V ^a   Jankowski, Piotr ^b  

^a BOWLING GREEN STATE UNIVERSITY   (United States)

^b SAN DIEGO STATE UNIVERSITY   (United States)

Author keywords

Forest roads; Land use management; Landslides; Rough sets; Rule based predictive models

Indexed keywords

DECISION SUPPORT SYSTEMS; KNOWLEDGE BASED SYSTEMS; LAND USE; ROADS AND STREETS; ROUGH SET THEORY;

DECISION RULES; FOREST ROADS; LAND-USE MANAGEMENT;

LANDSLIDES;

FOREST MANAGEMENT; LAND USE PLANNING; LANDSLIDE; MAP; MODELING; NUMERICAL METHOD; PLANNING METHOD; THEORETICAL STUDY;

CLEARWATER RIVER [IDAHO]; IDAHO; NORTH AMERICA; UNITED STATES;

EID: 38149106569     PISSN: 01989715     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compenvurbsys.2007.04.001     Document Type: Article

References (48)

1. Ahlqvist O., Keukelaar J., and Oukbir K. Rough and fuzzy geographical data integration. International Journal of Geographical Information Science 17 3 (2003) 223-234
2. Arciszewski T., and Ziarko W. Inductive learning in civil engineering: A rough sets approach. Microcomputers in Civil Engineering 5 1 (1990) 19-28
3. Beven K., and Kirkby M.J. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24 (1979) 43-69
4. Burton A., and Bathurst J.C. Physically based modelling of shallow landslide sediment yield at a catchement scale. Environmental Geology 35 (1998) 89-99
5. Burrough P.A., Wilson J.P., van Gaans P.F.M., and Hansen A.J. Fuzzy k-means classification of topo-climatic data as an aid to forest mapping in the Greater Yellowstone Area, USA. Landscape Ecology 16 (2001) 523-546
6. Carrara A. Multivariate models for landslide hazard evaluation. Mathematical Geology 15 (1983) 402-426
7. Carrara A., Cardinali M., Detti R., Guzzetti F., Pasqui V., and Reichenbach P. GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms 16 (1991) 427-445
8. Carrara A., Cardinali M., Guzzetti F., and Reichenbach P. GIS technology in mapping landslide hazard. In: Carrara A., and Guzzetti F. (Eds). Geographical information systems in assessing natural hazards (1995), Kluwer Academic Publishers, Dordrecht, The Netherlands 135-175
9. Chung C.F., Fabbri A.G., and van Westen C.J. Multivariate regression analysis for landslide hazard zonation. In: Carrara A., and Guzzetti F. (Eds). Geographical information systems in assessing natural hazards (1995), Kluwer Academic Publishers, Dordrecht, The Netherlands 107-133
10. Chung C.F., and Fabbri A.G. Probabilistic prediction model for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing 65 (1999) 1389-1399
11. Conrad, O. (2004). System for Automated Geographical Analysis (SAGA) software. Version 1.1. Department for Physical Geography, University of Göttingen (Germany), Accessed 12 June 2004, http://www.saga-gis.org.
12. Dempster A.P. Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics 38 (1967) 325-339
13. Dhakal A.S., Amada T., and Aniya M. Landslide hazard mapping and its evaluation using GIS: An investigation of sampling schemes for a grid-cell based quantitative method. Photogrammetric Engineering and Remote Sensing 66 (2000) 981-989
14. Duan J., and Grant G.E. Shallow landslide delineation for steep forest watersheds based on topographic attributes and probability analysis. In: Wilson J.P., and Gallant J.C. (Eds). Terrain analysis principles and applications (2000), Wiley, New York 311-329
15. Dyrness, C. T. (1967). Mass soil movements in the H.J. Andrews Experimental Forest. USDA Forest Service, Pacific Northwest Forest and Range Experiment Station, Research Paper PNW-42 (p. 12).
16. Elliot, W. J., Foltz, R. B., Luce, C. H., & Koler, T. E. (1996). Computer-aided risk analysis in road decommissioning. Watershed Restoration Management: Physical, chemical, and biological consideration. In Proceedings AWRA annual symposium, Syracuse, New York, July 14-17.
17. Fernandes N.F., Guimarães R.F., Gomes R.A.T., Vieira B.C., Montgomery D.R., and Greenberg H. Topographic controls of landslides in Rio de Janeiro: field evidence and modeling. Catena 55 (2004) 163-181
18. Glade T. Establishing the frequency and magnitude of landslide-triggering rainstorm events in New Zealand. Environmental Geology 35 (1998) 160-174
19. Gorsevski, P. V. (2002). Landslide hazard modeling using GIS. Ph.D. Dissertation, University of Idaho, Moscow, USA.
20. Gorsevski P.V., Gessler P.E., and Jankowski P. Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. Journal of Geographical Systems 5 (2003) 223-251
21. Gorsevski P.V., Gessler P.E., and Jankowski P. Spatial prediction of landslide hazard using fuzzy k-means and Bayes theorem. In: Widacki W., Bytnerowicz A., and Riebau A. (Eds). A message from the Tatra: Geographical Information Systems and remote sensing in Mountain Environmental Research (2004), Jagiellonian University Press, Krakow, Poland 159-172
22. Gorsevski P.V., Jankowski P., and Gessler P.E. Spatial prediction of landslide hazard using fuzzy k-means and Dempster-Shafer theory. Transactions in GIS 9 (2005) 455-474
23. Gorsevski P.V., Gessler P.E., Foltz R.B., and Elliot W.J. Spatial prediction of landslide hazard using logistic regression and ROC analysis. Transactions in GIS 10 3 (2006) 395-415
24. Gorsevski P.V., Gessler P.E., Boll J., Elliot W.J., and Foltz R.B. Spatially and temporally distributed modeling of landslide susceptibility. Geomorphology 80 3-4 (2006) 178-198
25. Gorsevski P.V., Jankowski P., and Gessler P.E. An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control and Cybernetics 35 1 (2006) 121-146
26. Hammond, C., Hall, D., Miller, S., & Swetik, P. (1992). Level I Stability Analyses (LISA) documentation for version 2.0. Gen. Technical Report INT-285, U.S. Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, UT (p. 190).
27. Komorowski J., and Øhrn A. Modelling prognostic power of cardiac tests using rough sets. Artificial Intelligence in Medicine 15 2 (1999) 167-191
28. McClelland, D. E., Foltz, R. B., Wilson, W. D., Cundy, T. W., Heinemann, R., Saurbier, J. A., et al. (1997). Assessment of the 1995 & 1996 floods and landslides on the Clearwater National Forest, Part I: Landslide Assessment. A Report to the regional Forester Northern Region U.S. Forest Service, December.
29. McKee T. Rough sets bankruptcy prediction models versus auditor signalling rates. Journal of Forecasting 22 (2003) 569-586
30. Montgomery D.R., and Dietrich W.E. A physically based model for the topographic control on shallow landsliding. Water Resources Research 30 (1994) 1153-1171
31. Moore I.D., Grayson R.B., and Ladson A.R. Digital terrain modeling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes 5 (1991) 3-30
32. Moore I.D., Gessler P.E., and Nielsen G.A. Soil attribute prediction using terrain analyses. Soil Science Society of American Journal 57 2 (1993) 443-452
33. Okimura T., and Ichikawa R. A prediction method for surface failures by movements of infiltrated water in a surface soil layer. Natural Disaster Science 7 (1985) 41-51
34. O'Loughlin E.M. Prediction of surface saturation zones in natural catchments by topographic analysis. Water Resources Research 22 5 (1986) 794-804
35. Pal S.K., and Mitra P. Multispectral image segmentation using the rough-set-initialized EM Algorithm. IEEE Transactions on Geoscience and Remote Sensing 40 11 (2002) 2495-2501
36. Pawlak Z. Rough sets. International Journal of Computer Information Sciences 11 5 (1982) 341-356
37. Pawlak Z. Rough sets - Theoretical aspects of reasoning about data (1991), Kluwer Academic Publishers, Dordrecht, The Netherlands
38. Pawlak, Z., & Slowinski, R. (1993). Rough set approach to multi-attribute decision analysis. ICS Research Report 36, Warsaw University of Technology.
39. Pawlak Z., and Slowinski R. Rough set approach to multi-attribute decision analysis. European Journal of Operational Research 72 (1994) 443-459
40. Predki B., Slowinski R., Stefanowski J., Susmaga R., and Wilk Sz. ROSE - Software implementation of the Rough Set Theory. In: Polkowski L., and Skowron A. (Eds). Rough sets and current trends in computing. Lecture notes in artificial intelligence Vol. 1424 (1998), Springer, Berlin 605-608
41. Predki B., and Wilk Sz. Rough set based data exploration using ROSE system. In: Ras Z.W., and Skowron A. (Eds). Foundations of intelligent systems. Lecture notes in artificial intelligence Vol. 1609 (1999), Springer, Berlin 172-180
42. Shafer G. A mathematical theory of evidence (1976), Princeton University Press, Princeton
43. Sidle, R. C., Pearce, A. J., & O'Loughlin, C. L. (1985). Hillslope stability and landuse. American Geophysical Union, Water Resource Monograph No. 11, Washington, DC.
44. Slowinski R., and Zopounidis C. Application of the rough set approach to evaluation of bankruptcy risk. International Journal of Intelligent Systems in Accounting, Finance and Management 4 1 (1995) 27-41
45. Slowinski R., Stefanowski J., Greco S., and Matarazzo B. Rough sets based processing of inconsistent information in decision analysis. Control and Cybernetics 29 (2000) 379-404
46. Wilson J.P., and Gallant J.C. Digital terrain analysis. In: Wilson J.P., and Gallant J.C. (Eds). Terrain analysis principles and applications (2000), Wiley, New York 1-29
47. Wu W., and Sidle R.C. A distributed slope stability model for steep forested basins. Water Resources Research 31 (1995) 2097-2110
48. Ziarko W., and Shan N. An incremental learning algorithm for constructing decision rules. In: Ziarko W.P. (Ed). Rough sets, fuzzy sets and knowledge discovery (1994), Springer, Berlin 326-334