Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • AlFugara AM, Pradhan B and Mohamed TA (2009) Enhancement of satellite image classification using object oriented and fuzzy logic approach. Applied Geomatics, artificial neural networks and their comparison with frequency ratio and bivariate logistic regression. Environ Modell Softw 25(6): 747–759

    Google Scholar 

  • Atkinson PM and Massari R (1998) Generalized linear modeling of susceptibility to land sliding in the central Apennines, Italy. Comput Geosci 24:373–385

    Article  Google Scholar 

  • Baeza C and Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Proc Land 26:1127–1263

    Article  Google Scholar 

  • Bonham-Carter GF (1994) Geographic Information Systems for Geoscientists: Modelling with GIS.

  • Carro M, De Amicis M, Luzi L and 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, 69:139–159

    Article  Google Scholar 

  • Chow WS, Zakaria M, Ferdaus A Nurzaidi A (2003) Geological Terrain Mapping. JMG Unpublished Report, JMG.SWP.GS 16/2003

  • Chung CF and Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472

    Article  Google Scholar 

  • Clerici A, Perego S, Tellini C and Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology, 48:349–364

    Article  Google Scholar 

  • Cobbing EJ, Pitfield PEJ, Darbyshire DPE and Mallick, DIJ (1992) The Granites of Southeast Asian tin belt. British Geolological Survey Oversea Memoir 10

  • Dai FC and Lee, CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

    Article  Google Scholar 

  • Dai FC, Lee CF, Li J and Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391

    Article  Google Scholar 

  • Donati L and Turrini MC (2002) An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology, application to an area of the Apennines (Valnerina; Perugia, Italy). Eng Geol 63:277–289

    Article  Google Scholar 

  • Ercanoglu M and Gokceoglu, C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkye) by fuzzy approach. Environ Geol 41:720–730

    Article  Google Scholar 

  • Gokceoglu C Sonmez, H and Ercanoglu M (2000) Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey. Eng Geol 55:277–296

    Article  Google Scholar 

  • Guzzetti F Carrarra, A Cardinali M and Reichenbach, P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study. Central Italy. Geomorphology 31:181–216

    Article  Google Scholar 

  • Jibson WR Edwin, LH and John, AM (2000) A method for producing digital probabilistic seismic landslide hazard maps. Eng Geol 58:271–289

    Article  Google Scholar 

  • Lee S and Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113

    Article  Google Scholar 

  • Lee S, Chwae U and Min K (2002a) Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea. Geomorphology 46:49–162

    Google Scholar 

  • Lee S, Choi J and Min K (2002b) Landslide susceptibility analysis and verification using the Bayesian probability model. Environ Geol 43:120–131

    Article  Google Scholar 

  • Lee S, Ryu JH, Min K and Won JS (2003a) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Proc Land 27:1361–1376

    Article  Google Scholar 

  • Lee S, Ryu JH, Lee MJ and Won JS (2003b) Landslide susceptibility analysis using artificial neural network at Boun, Korea. Environ Geol 44:820–833

    Article  Google Scholar 

  • Lee S and Choi U (2003c) Development of GIS-based geological hazard information system and its application for landslide analysis in Korea. Geoscience Journal 7:243–252

    Article  Google Scholar 

  • Lee S, Ryu JH, Won JS and Park HJ (2004a) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302

    Article  Google Scholar 

  • Lee S, Choi J and Min K (2004b) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. Int J Remote Sens 25:2037–2052

    Article  Google Scholar 

  • Lee S and Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  • Lee S and Pradhan B (2006) Probabilistic landslide risk mapping at Penang Island, Malaysia. J Earth Syst Sci 115(6):661–672

    Article  Google Scholar 

  • Luzi L, Pergalani F and Terlien MTJ (2000) Slope vulnerability to earthquakes at sub-regional scale, using probabilistic techniques and geographic information systems. Eng Geol 58:313–336

    Article  Google Scholar 

  • Ohlmacher GC and Davis, JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansa, USA. Eng Geol 69:331–343

    Article  Google Scholar 

  • Parise M and Jibson WR (2000) A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake. Eng Geol 58:251–270

    Article  Google Scholar 

  • Pistocchi A Luzi, L and Napolitano P (2002) The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol 41:765–775

    Article  Google Scholar 

  • Pradhan B and Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis by using an artificial neural network model. Earth Science Frontier 14(6):143–152

    Article  Google Scholar 

  • Pradhan B and Lee S (2009) Landslide risk analysis using artificial neural network model focusing on different training sites. Int J Phys Sci 3(11):1–15.

    Google Scholar 

  • Pradhan B (2010) 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 45(10): 1244–1256

    Article  Google Scholar 

  • Pradhan B and Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sciences 60:1037–1054

    Article  Google Scholar 

  • Pradhan B and Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Modell Softw 25(6): 747–759

    Article  Google Scholar 

  • Pradhan B and Lee S (2010c) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1): 13–30

    Article  Google Scholar 

  • Pradhan B, Lee S and Buchroithner MF (2009) Use of geospatial data for the development of fuzzy algebraic operators to landslide hazard mapping: a case study in Malaysia. Applied Geomatics 1:3–15

    Article  Google Scholar 

  • Pradhan B, Lee S and 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 1:17–32.

    Article  Google Scholar 

  • Pradhan B, Lee S and Buchroithner MF (2010b) A GIS-based back-propagation neural network model and its cross application and validation for landslide susceptibility analyses. Comput Environ Urban 34:216–235.

    Article  Google Scholar 

  • Pradhan B, Lee S, Mansor S, Buchroithner MF, Jallaluddin N and 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 2:1–11

    Article  Google Scholar 

  • Pradhan B, Singh RP and Buchroithner MF (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Adv Space Res 37:698–709

    Article  Google Scholar 

  • Rautelal P and Lakhera RC (2000) Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India). International Journal of Applied Earth Observation and Geoinformation 2:153–160

    Article  Google Scholar 

  • Rece A and Capolongo D (2002) Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Comput Geosci 28:735–749

    Article  Google Scholar 

  • Remondo J, Gonzalez A, Diaz de Teran JR, Cendrero A, Fabbri AG and Chung CJF (2003) Validation of landslide susceptibility maps: examples and applications from a case study in Northern Spain. Nat Hazards 30:437–449

    Article  Google Scholar 

  • Romeo R (2000) Seismically induced landslide displacements: a predictive model. Eng Geol 58:337–351

    Article  Google Scholar 

  • Rowbotham D and Dudycha DN (1998) GIS modeling of slope stability in Phewa Tal watershed, Nepal. Geomorphology 26:151–170

    Article  Google Scholar 

  • Shou KJ and Wang CF (2003) Analysis of the Chiufengershan landslide triggered by the 1999 Chi-Chi earthquake in Taiwan. Eng Geol 68:237–250

    Article  Google Scholar 

  • Temesgen B Mohammed, MU and Korme T (2001) Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet area, Ethiopia. Phys Chem Earth (C) 26:665–675

    Google Scholar 

  • Vijith H and Madhu G (2008) Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environ Geol 55(7):1397–1405

    Article  Google Scholar 

  • Vijith H and Madhu G (2007) Application of GIS and frequency ratio model in mapping the potential surface failure sites in the Poonjar sub-watershed of Meenachil river in Western ghats of Kerala. Indian Soc Remote Sens, 35(3):275–285

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Gaber AFD and Buchroithner, MF (2009). Geomorphological hazard analysis along the Egyptain red sea coast between Safaga and Quseir. Nat Hazard Earth Sys 9(9):751–766

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Information and Control 8:338–253

    Article  Google Scholar 

  • Zhou G. Esaki T. Mitani Y. Xie M and Mori J (2003) Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Eng Geol 68:373–386

    Article  Google Scholar 

  • Zhou CH, Lee CF, Li J and Xu ZW (2002) On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology 43:197–207

    Article  Google Scholar 

  • Zimmerman HZ (1996) Fuzzy sets theory and its applications, Kluwer Academic Publishers, Dordrecht

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Cartography, Dresden University of Technology, 01082, Dresden, Germany

    B. Pradhan

Authors
  1. B. Pradhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. Pradhan.

About this article

Cite this article

Pradhan, B. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38, 301–320 (2010). https://doi.org/10.1007/s12524-010-0020-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-010-0020-z

Keywords

Search

Navigation