Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping

被引:110
作者
Pradhan B. [1 ]
Lee S. [2 ]
Buchroithner M.F. [3 ]
机构
[1] Institute of Cartography, Dresden University of Technology
[2] Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), Yusung-Gu, Daejon, 30, Kajung-Dong
[3] Faculty of Forestry, Geo and Hydro-Science, Dresden University of Technology
关键词
Frequency ratio; Fuzzy membership; Fuzzy operator; GIS; Hazard; Landslide; Malaysia;
D O I
10.1007/s12518-009-0001-5
中图分类号
学科分类号
摘要
Geospatial data base creation for landslidehazard mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslidehazard analysis was modeled on the basis of spatially related factors. This paper presents the use of fuzzy logic to landslide-hazard analysis in the Penang Island, Malaysia, using remote sensing data and a geographic information system (GIS). To achieve the goal, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined for landslide-hazard analysis. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and field surveys. The nine factors that influence landslide occurrence were extracted from the database and the frequency ratio coefficient for each factor was computed. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslidehazard mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ=0.8) showed the best accuracy (80%) while the case in which the fuzzy or operator was applied showed the worst accuracy (56%). © Società Italiana di Fotogrammetria e Topografia (SIFET) 2009.
引用
收藏
页码:3 / 15
页数:12
相关论文
共 47 条
[31]  
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 at Selangor, Malaysia, Earth Science Frontier, 14, 6, pp. 143-152, (2007)
[32]  
Pradhan B., Lee S., Landslide risk analysis using artificial neural network model focusing on different training sites. International, Journal of Physical Sciences, 3, 11, pp. 1-15, (2008)
[33]  
Pradhan B., Singh R.P., Buchroithner M.F., Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data, Adv Space Res, 37, pp. 698-709, (2006)
[34]  
Pradhan B., Lee S., Mansor S., Buchroithner M., Jamaluddin M., Khujaimah Z., Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model, J Appl Remote Sens, 2, pp. 1-11, (2008)
[35]  
Refice A., Capolongo D., Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment, Computers and Geosciences, 28, 6, pp. 735-749, (2002)
[36]  
Romeo R., Seismically induced landslide displacements: A predictive model, Eng Geol, 58, pp. 337-351, (2000)
[37]  
Rowbotham D.N., Dudycha D., GIS modelling of slope stability in Phewa Tal watershed, Nepal, Geomorphology, 26, 1-3, pp. 151-170, (1998)
[38]  
Shou K.J., Wang C.F., Analysis of the Chiufengershan landslide triggered by the 1999 Chi-Chi earthquake in Taiwan, Eng Geol, 68, pp. 237-250, (2003)
[39]  
Suzen M.L., Doyuran V., A comparison of the GIS based landslide susceptibility assessment methods: Multivariate versus bivariate, Environmental Geology, 45, 5, pp. 665-679, (2004)
[40]  
Tangestani M.H., Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran, Australian Journal of Earth Sciences, 51, 3, pp. 439-450, (2004)