Environmental Earth Sciences

, Volume 63, Issue 2, pp 329–349 | Cite as

Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia

Original Article


Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.


Landslides Susceptibility Fuzzy relations Remote sensing GIS Cross application Malaysia 



Thanks to the Alexander von Humboldt Foundation (AvH), Germany for awarding visiting scientist position to carry out research at Dresden University of Technology, Germany. This article is greatly benefited from very helpful reviews by two anonymous reviewers and editorial comments by James W. LaMoreaux.


  1. Ahmad F, Yahaya AS, Farooqi MA (2006) Characterization and geotechnical properties of Penang residual soils with emphasis on landslides. Am J Environ Sci 2(4):121–128CrossRefGoogle Scholar
  2. 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(6):1127–1143CrossRefGoogle Scholar
  3. Angillieri MYE (2010) Application of frequency ratio and logistic regression to active rock glacier occurrence in the Andes of San Juan, Argentina. Geomorphology 114(3):396–405CrossRefGoogle Scholar
  4. Begueria S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329CrossRefGoogle Scholar
  5. Bignel F, Snelling G (1977) The geochronology of the Main Range Batholith: Cameron Highlands road and Gunong Bujang Melaka. Overseas Geol Miner Resour 47:3–35Google Scholar
  6. Bonham-Carter GF (1994) Geographic information systems for geoscientists. Modelling with GISGoogle Scholar
  7. Champatiray PK, Dimri S, Lakhera RC, Sati S (2007) Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4:101–111CrossRefGoogle Scholar
  8. Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetr Eng Remote Sensing 65(12):1389–1399Google Scholar
  9. Clerici A, Perego S, Tellini C, Vescovi PA (2006) GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environ Geol 50:941–961CrossRefGoogle Scholar
  10. Dahal RK, Hasegawa S, Nonomura S, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54(2):314–324CrossRefGoogle Scholar
  11. Das I, Sahoo S, Van Westen C, Stein A, 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(4):627–637CrossRefGoogle Scholar
  12. Deleo JM (1993) Receiver operating characteristic laboratory (ROCLAB): software for developing decision strategies that account for uncertainty. In: Proceedings of the 2nd international symposium on uncertainty modelling and analysis. Computer Society Press, College Park, pp 318–325Google Scholar
  13. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730CrossRefGoogle Scholar
  14. Erener A, Duzgun HSB (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7(1):55–68CrossRefGoogle Scholar
  15. Gokceoglu C, Sonmez H, Ercanoglu M (2000) Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey. Eng Geol 55:277–296CrossRefGoogle Scholar
  16. Guzzetti F, Carrarra A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216CrossRefGoogle Scholar
  17. Hines JW (1997) Fuzzy and neural approaches in engineering. Wiley, New York, p 210Google Scholar
  18. Lamelas MT, Marinoni O, Hoppe A, Riva J (2008) Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain). Environ Geol 54(5):963–977CrossRefGoogle Scholar
  19. Lee S (2007a) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Process Landf 32(14):2133–2148CrossRefGoogle Scholar
  20. Lee S (2007b) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52:615–623CrossRefGoogle Scholar
  21. Lee S, Dan NT (2005) Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environ Geol 48:778–787CrossRefGoogle Scholar
  22. Lee S, Evangelista DG (2006) Earthquake induced landslide susceptibility mapping using an artificial neural network. Nat Hazard Earth Syst 6:687–695CrossRefGoogle Scholar
  23. Lee S, Pradhan B (2006) Probabilistic landslide risk mapping at Penang Island. Malays J Earth Syst Sci 115(6):1–12Google Scholar
  24. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41CrossRefGoogle Scholar
  25. Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38(2):199–220CrossRefGoogle Scholar
  26. Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4(4):327–338CrossRefGoogle Scholar
  27. Mansor S, Pradhan B, Daud M, Jamaludin N, Khuzaimah Z (2007) Landslide susceptibility analysis using an artificial neural network. In: Ehlers M, Michel U (eds) Proceedings of SPIE, vol 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, 67490J, pp 1–7Google Scholar
  28. Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the Metropolitan area of Istanbul, Turkey. Math Probl Eng. doi: 10.1155/2010/901095
  29. Oh HJ, Lee S, Chotikasathien W, Kim CH, Kwon JH (2009) Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand. Environ Geol 57:641–651CrossRefGoogle Scholar
  30. Pradhan B (2010a) Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res. doi: 10.1016/j.asr.2010.01.006
  31. Pradhan B (2010b) Manifestation of an advanced fuzzy logic model coupled with Geoinformation techniques for landslide susceptibility analysis. Environ Ecol Stat. doi:  10.1007/s10651-010-0147-7
  32. Pradhan B (2010c) Application of an advanced fuzzy logic model for landslide susceptibility analysis. Int J Comput Intell Sys 3(3):370–381Google Scholar
  33. Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16(2):107–126CrossRefGoogle Scholar
  34. Pradhan B, Lee S (2007) 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 Sci Front 14(6):143–152CrossRefGoogle Scholar
  35. Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focusing on different training sites. Int J Phys Sci 3(11):1–15Google Scholar
  36. Pradhan B, Lee S (2010a) Regional landslide susceptibility analysis using backpropagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30CrossRefGoogle Scholar
  37. Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Modell Softw 25:747–759CrossRefGoogle Scholar
  38. Pradhan B, Lee S (2010c) Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environ Earth Sci 60:1037–1054Google Scholar
  39. Pradhan B, Pirasteh S (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping. Disaster Adv 3(2):26–34Google Scholar
  40. Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS for landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3(3):319–326Google Scholar
  41. Pradhan B, Singh RP, Buchroithner MF (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Adv Space Res 37:698–709CrossRefGoogle Scholar
  42. Pradhan B, Lee S, Mansor S, Buchroithner MF, Jallaluddin N, Khujaimah Z (2008) 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 2:1–11CrossRefGoogle Scholar
  43. Pradhan B, Lee S, Buchroithner MF (2009) Use of geospatial data for the development of fuzzy algebraic operators to landslide hazard mapping: a case study in Malaysia. Appl Geomatics 1:3–15CrossRefGoogle Scholar
  44. Pradhan B, Lee S, Buchroithner MF (2010a) Remote sensing and GIS-based landslide susceptibility analysis and its cross-validation in three test areas using a frequency ratio model. Photogramm Fernerkun 1(1):17–32Google Scholar
  45. Pradhan B, Lee S, Buchroithner MF (2010b) A GIS-based back-propagation neural network model and its cross application and validation for landslide susceptibility analyses. Comput Environ Urban 34:216–235CrossRefGoogle Scholar
  46. Pradhan B, Sezer E, Gokceoglu C, Buchroithner MF (2010c) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE T Geosci Remote 48(10). doi: 10.1109/TGRS.2010.2050328
  47. Saboya F Jr, Alves MG, Pinto WD (2006) Assessment of failure susceptibility of soil slopes using fuzzy logic. Eng Geol 86:211–224CrossRefGoogle Scholar
  48. Shaluf IM, Ahmadun FR (2006) Disaster types in Malaysia: an overview. Disaster Prev Manage 15(2):286–298CrossRefGoogle Scholar
  49. Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679CrossRefGoogle Scholar
  50. Tangestani MH (2004) Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Aust J Earth Sci 51:439–450CrossRefGoogle Scholar
  51. Tunusluoglu MC, Gokceoglu C, Nefeslioglu HA, Sonmez H (2007) Extraction of potential debris source areas by logistic regression technique: a case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey). Environ Geol 54(1):9–22CrossRefGoogle Scholar
  52. Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. Nat Hazards 3:63Google Scholar
  53. Wang HB, Sassa K (2005) Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environ Geol 47:956–966CrossRefGoogle Scholar
  54. Xie M, Esaki T, Cai M (2004) A time-space based approach for mapping rainfall-induced shallow landslide hazard. Environ Geol 46:840–850CrossRefGoogle Scholar
  55. Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582CrossRefGoogle Scholar
  56. Youssef AM, Pradhan B, Gaber AFD, Buchroithner MF (2009) Geomorphological hazard analysis along the Egyptian Red Sea coast between Safaga and Quseir. Nat Hazard Earth Syst 9:751–766CrossRefGoogle Scholar
  57. Zadeh LA (1965) Fuzzy sets. Inf Control 8:253–338CrossRefGoogle Scholar
  58. Zhou G, Esaki T, Mitani Y, Xie M, Mori J (2003) Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Eng Geol 68:373–386CrossRefGoogle Scholar
  59. Zimmerman HZ (1996) Fuzzy sets theory and its applications. Kluwer, DordrechtGoogle Scholar
  60. Zimmerman H-J, Zysno P (1980) Latent connectivities in human decision making. Fuzzy sets Syst 4:37–51Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Institute for Cartography, Faculty of Forestry, Geo and Hydro-ScienceDresden University of TechnologyDresdenGermany

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