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Environmental Earth Sciences

, Volume 60, Issue 5, pp 1037–1054 | Cite as

Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models

  • Biswajeet Pradhan
  • Saro Lee
Original Article

Abstract

This paper summarizes findings of landslide hazard analysis on Penang Island, Malaysia, using frequency ratio, logistic regression, and artificial neural network models with the aid of GIS tools and remote sensing data. Landslide locations were identified and an inventory map was constructed by trained geomorphologists using photo-interpretation from archived aerial photographs supported by field surveys. A SPOT 5 satellite pan sharpened image acquired in January 2005 was used for land-cover classification supported by a topographic map. The above digitally processed images were subsequently combined in a GIS with ancillary data, for example topographical (slope, aspect, curvature, drainage), geological (litho types and lineaments), soil types, and normalized difference vegetation index (NDVI) data, and used to construct a spatial database using GIS and image processing. Three landslide hazard maps were constructed on the basis of landslide inventories and thematic layers, using frequency ratio, logistic regression, and artificial neural network models. Further, each thematic layer’s weight was determined by the back-propagation training method and landslide hazard indices were calculated using the trained back-propagation weights. The results of the analysis were verified and compared using the landslide location data and the accuracy observed was 86.41, 89.59, and 83.55% for frequency ratio, logistic regression, and artificial neural network models, respectively. On the basis of the higher percentages of landslide bodies predicted in very highly hazardous and highly hazardous zones, the results obtained by use of the logistic regression model were slightly more accurate than those from the other models used for landslide hazard analysis. The results from the neural network model suggest the effect of topographic slope is the highest and most important factor with weightage value (1.0), which is more than twice that of the other factors, followed by the NDVI (0.52), and then precipitation (0.42). Further, the results revealed that distance from lineament has the lowest weightage, with a value of 0. This shows that in the study area, fault lines and structural features do not contribute much to landslide triggering.

Keywords

Landslide Hazard Frequency ratio Logistic regression Artificial neural network GIS Malaysia 

Notes

Acknowledgments

B. Pradhan would like to thank the Alexander von Humboldt Foundation (AvH), Germany for awarding a visiting scientist position and adequate funds to carry out research at Dresden University of Technology, Germany. Thanks are due to anonymous reviewers for their critical and valuable comments that helped to bring the manuscript into the present form.

References

  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. Akgul A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol 51(8):1377–1387CrossRefGoogle Scholar
  3. 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):1127–1143(17)CrossRefGoogle Scholar
  4. Atkinson PM, Massari R (1998) Generalized linear modeling of susceptibility to landsliding in the central Apennines. Italy Comput Geosci 24(4):373–385CrossRefGoogle Scholar
  5. Begueria S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329CrossRefGoogle Scholar
  6. Carro M, De Amicis M, Luzi L, Marzorati S (2003) The application of predictive modeling techniques to landslides induced by earthquakes: the case study of the 26 September 1997 Umbria- Marche earthquake (Italy). Eng Geol 1(2):139–159CrossRefGoogle Scholar
  7. Chan NW (1998a) Environmental hazards associated with hill land development in Penang Island, Malaysia: some recommendations on effective management. Disaster Prev Manage Int J 7(4):305–318CrossRefGoogle Scholar
  8. Chan NW (1998) Responding to landslide hazards in rapidly developing Malaysia: a case of economics versus environmental protection. Disaster Prev Manage Int J 7(1)Google Scholar
  9. Cheng TA, Lateh H, Peng KS (2008) Intelligence explanation system on landslide dissemination: a case study in Malaysia. In: Proceddings of the first world landslide forum report: Implementing the 2006 Tokyo action plan on the international program on landslides (IPL). 330–333Google Scholar
  10. Choi J, Oh HJ, Won JS, Lee S (2009) Validation of an artificial neural network model for landslide susceptibility mapping. Environ Earth Sci. doi: 10.1007/s12665-009-0188-0
  11. Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetric Eng Remote Sens 65(12):1389–1399Google Scholar
  12. Clerici A, Perego S, Tellini C, Vescovi PA (2002) Procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48:349–364CrossRefGoogle Scholar
  13. 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
  14. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228CrossRefGoogle Scholar
  15. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkye) by fuzzy approach. Environ Geol 41:720–730CrossRefGoogle Scholar
  16. 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
  17. 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
  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 (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491CrossRefGoogle Scholar
  20. Lee S (2007) 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, Pradhan B (2006) Probabilistic landslide risk mapping at Penang Island. Malaysia J Earth Syst Sci 115(6):1–12Google Scholar
  23. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41CrossRefGoogle Scholar
  24. Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990CrossRefGoogle Scholar
  25. Lloyd DM, Othman A, Wilkinson PL, Anderson MG (2001) Predicting landslides: Assessment of an automated rainfall based landslide warning system. In: K.K.S.Ho and K.S.Li, Geotechnical Engineering—Meeting Society’s Needs, Balkema, 1, 135–139Google Scholar
  26. Ong WS (1993) The geology and engineering geology of Penang Island. Geological Survey of Malaysia, MalaysiaGoogle Scholar
  27. Ooi LH (1999) Rockfall protection: Technical talks, Kuala Lumpur, 28 Oct. 1999, Geological Society of Malaysia, pp 20–32Google Scholar
  28. Paola JD, Schowengerdt RA (1995) A review and analysis of backpropagation neural networks for classificatioin of remotely sensed multi-spectral imagery. Int J Remote Sens 16:3033–3058CrossRefGoogle Scholar
  29. Pradhan B, Lee S (2008a) 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):143–152Google Scholar
  30. Pradhan B, Lee S (2008b) Landslide risk analysis using artificial neural network model focusing on different training sites. Int J Phys Sci 3(11):1–15Google Scholar
  31. 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
  32. Pradhan B, Lee S, Mansor S, Buchroithner MF, Jallaluddin N (2008) Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. Appl Remote Sens 2:1–11Google Scholar
  33. 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
  34. Refice A, Capolongo D (2002) Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Comput Geosci 28:735–749CrossRefGoogle Scholar
  35. Romeo R (2000) Seismically induced landslide displacements: a predictive model. Eng Geol 58:337–351CrossRefGoogle Scholar
  36. Sassa K (2008) Land-use and Landslides – Human-Induced Disasters. Proceddings of the first world landslide forum report: implementing the 2006 Tokyo action plan on the international program on landslides (IPL), pp 1–20Google Scholar
  37. Sin HT, Chan NW (2004) The urban heat island phenomenon in Penang Island: Some observations during the wet and dry season. In: Jamaluddin Md. Jahi, Kadir Arifin, Salmijah Surif and Shaharudin Idrus (eds.). Proceedings 2nd. Bangi World Conference on Environmental Management. Facing Changing Conditions. 13–14 September 2004, Bangi, Malaysia, pp 504–516Google Scholar
  38. Süzen ML, Doyuran VA (2004) Comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679CrossRefGoogle Scholar
  39. Tan BK (1990) Studies on the characteristics of soils and rocks and slope stability in urban areas of Penang island. Research Report, July 1990, Univ. Kebangsaan Malaysia, Bangi, (in Malay). 1990. 80–86Google Scholar
  40. Toh CT (1999) Influence of geology and geological structures on cut slope stability: Technical Talks, Kuala Lumpur, 28 Oct 1999, Geological Society of Malaysia, pp 12–25Google Scholar
  41. Tunusluoglu MC, Gokceoglu C, Nefeslioglu HA, Sonmez H (2008) 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
  42. Turban E, Aronson JE (2001) Decision Support Systems and Intelligent Systems. Prentice Hall, New JerseyGoogle Scholar
  43. Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. Nat Hazards 3:63Google Scholar
  44. Wang HB, Sassa K (2005) Comparative evaluation of landslide susceptibility in Minamata area. Jpn Environ Geol 47:956–966CrossRefGoogle Scholar
  45. Youssef AM, Pradhan B, Gaber AFD, Buchroithner MF (2009) Geomorphological Hazard Analysis along the Egyptian Red Sea Coast between Safaga and Quseir. Natural Hazards and Earth System Science, pp 751–766Google Scholar
  46. 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

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Faculty of Forestry, Geo and Hydro-Science, Institute of CartographyDresden University of TechnologyDresdenGermany
  2. 2.Geoscience Information CenterKorea Institute of Geoscience and Mineral Resources (KIGAM)DaejonKorea

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