Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility modelling applying user-defined weighting and data-driven statistical techniques in Cox’s Bazar Municipality, Bangladesh

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

Landslides are common geophysical hazards in the highly urbanized hilly areas of Cox’s Bazar Municipality, Bangladesh. Every year, during the monsoon, landslides cause human casualties, property damage, and economic losses. Indiscriminate hill cutting, developing settlements in dangerous hill slopes, and torrential rainfall in short period of time are responsible for triggering landslide disasters. The aim of this paper is to produce landslide susceptibility maps (LSM) to help reduce the risks of landslides. Geographic information system and remote sensing-based techniques were used for LSM considering 12 relevant factor maps (i.e. slope, land cover, NDVI, geology, geomorphology, soil moisture, rainfall pattern, distance from road, drain, stream, structure, and faults–lineaments). For the modelling purpose, four techniques were implemented—artificial hierarchy process (AHP), weighted linear combination (WLC), logistic regression (LR), and multiple logistic regression (MLR). A landslide inventory map with 74 historical landslide locations was prepared by field surveying. The modelling results are validated using the area under the relative operating characteristics curves (AUC). AUC values of AHP, WLC, LR, and MLR methods are calculated as 88, 85.90, 74.90, and 90.40 %, respectively.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  • Agresti A (2007) An introduction to categorical data analysis (Chapter 4), 2nd edn. Wiley, Hoboken

    Book  Google Scholar 

  • Ahmed B (2011) Urban land cover change detection analysis and modeling spatio-temporal growth dynamics using remote sensing and GIS techniques: a case study of Dhaka, Bangladesh. Masters Dissertation. University of New Lisbon (UNL), Lisbon, Portugal

  • Ahmed B (2014) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides. doi:10.1007/s10346-014-0521-x

  • Ahmed B, Ahmed R (2012) Modeling urban land cover growth dynamics using multi-temporal satellite images: a case study of Dhaka, Bangladesh. ISPRS Int J Geo-Inf 1(1):3–31

    Article  Google Scholar 

  • Ahmed B, Rubel YA (2013) Understanding the issues involved in urban landslide vulnerability in Chittagong Metropolitan Area, Bangladesh. Association of American Geographers (AAG), Washington

    Google Scholar 

  • Ahmed B, Ahmed R, Zhu X (2013a) Evaluation of model validation techniques in land cover dynamics. ISPRS Int J Geo-Inf 2(3):577–597

    Article  Google Scholar 

  • Ahmed B, Kamruzzaman M, Zhu X, Rahman M, Choi K (2013b) Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sens 5(11):5969–5998

    Article  Google Scholar 

  • Ahmed B, Rahman MS, Rahman S, Huq FF, Ara S (2014) Landslide inventory report of Chittagong Metropolitan Area, Bangladesh. BUET-Japan Institute of Disaster Prevention and Urban Safety (BUET-JIDPUS); Bangladesh University of Engineering and Technology (BUET), Dhaka-1000, Bangladesh. http://www.landslidebd.com/reports/. Accessed on 23 June 2015

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey. Landslides 9:93–106

    Article  Google Scholar 

  • Alexander D (1991) Information technology in real-time for monitoring and managing natural disasters. Prog Phys Geogr 15:238. doi:10.1177/030913339101500302

    Article  Google Scholar 

  • Alexander DE (2000) Confronting catastrophe: new perspective on natural disasters (Chapter 9), 1st edn. Terra Publishing, Hertfordshire

    Google Scholar 

  • Alexander DE (2004). Vulnerability to landslides. In: Glade T, Anderson M, Crozier MJ (eds) Landslide hazard and risk. Wiley, New York. doi:10.1002/9780470012659

  • Alexander D (2006) Globalization of disaster: trends, problems and dilemmas. J Int Aff 59(2):1–22

    Google Scholar 

  • Alexander D (2007) Making research on geological hazards relevant to stakeholders’ needs. Quatern Int 171–172:186–192

    Article  Google Scholar 

  • Alexander DE (2008) A brief survey of GIS in mass-movement studies, with reflections on theory and methods. Geomorphology 94:261–267

    Article  Google Scholar 

  • Alexander DE (2014) Assessing vulnerability in Europe and the world. In: Birkmann J (ed) Assessment of vulnerability to natural hazards: a European perspective. Elsevier, San Diego

    Google Scholar 

  • ArcGIS® 10.2 Help (2014) Environmental systems research institute (ESRI). Redlands, CA, USA. http://resources.arcgis.com/en/help/main/10.2/. Accessed 12 February 2015

  • Armaş A (2012) Weights of evidence method for landslide susceptibility mapping Prahova Subcarpathians, Romania. Nat Hazards 60(3):937–950

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. doi:10.1016/j.geomorph.2004.06.010

    Article  Google Scholar 

  • Aydi A, Zairi M, Dhia HB (2013) Minimization of environmental risk of landfill site using fuzzy logic, analytical hierarchy process, and weighted linear combination methodology in a geographic information system environment. Environ Earth Sci 68:1375–1389. doi:10.1007/s12665-012-1836-3

    Article  Google Scholar 

  • Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River basin case study, Italy. Math Geosci 44(1):47–70

    Article  Google Scholar 

  • BBS (2013) District statistics 2011: Cox’s Bazar. Bangladesh Bureau of Statistics (BBS), Statistics and Informatics Division (SID), Ministry of Planning, Dhaka, Government of the People’s Republic of Bangladesh

  • Birkmann J (ed) (2006) Measuring vulnerability to natural hazards: towards disaster resilient societies, 1st edn. United Nations University Press, Tokyo

    Google Scholar 

  • Birkmann J, Cardona OD, Carreno ML, Barbat AH, Pelling M, Schneiderbauer S, Kienberger S, Keiler M, Alexander D, Zeil P, Welle T (2013) Framing vulnerability, risk and societal responses: the MOVE framework. Nat Hazards 67:193–211. doi:10.1007/s11069-013-0558-5

    Article  Google Scholar 

  • BUET-JIDPUS (2015) Developing dynamic web-GIS based early warning system for the communities at landslide risks in Chittagong Metropolitan Area, Bangladesh. BUET-Japan Institute of Disaster Prevention and Urban Safety (BUET-JIDPUS); Bangladesh University of Engineering and Technology (BUET), Dhaka-1000, Bangladesh. http://www.landslidebd.com/warning-system/. Accessed on 24 June 2015

  • CDMP-II (2012) Comprehensive disaster management programme‐II (CDMP-II). Landslide inventory and land-use mapping, DEM preparation, precipitation threshold value and establishment of early warning devices. Ministry of Food and Disaster Management, Disaster Management and Relief Division, Government of the People’s Republic of Bangladesh

  • Couture R (2011). Landslide terminology—national technical guidelines and best practices on landslides. Geological survey of Canada, Open file 6824, 12 p

  • Cruden DM (1991) A simple definition of landslide. Bull Int As Eng Geol 43:27–29

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3–4):213–228

    Article  Google Scholar 

  • Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat Hazards 65:135–165. doi:10.1007/s11069-012-0347-6

    Article  Google Scholar 

  • Eastman R (2012) IDRISI Selva help system. Clark Labs, Clark University, Worcester

    Google Scholar 

  • Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmialake basin, Iran. Nat Hazards 65:2105–2128. doi:10.1007/s11069-012-0463-3

    Article  Google Scholar 

  • Felicísimo Á, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189. doi:10.1007/s10346-012-0320-1

    Article  Google Scholar 

  • Guzzetti F, Carrara 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 31:181–216

    Article  Google Scholar 

  • Hossain, M. (2012). Cox’s Bazar. In: Islam N (ed) Banglapedia, national encyclopaedia of Bangladesh, 2nd edn. Banglapedia Trust, Asiatic Society of Bangladesh, Dhaka, Bangladesh. http://www.banglapedia.org/HT/C_0432.htm. Accessed on 05 Feb 2015

  • ICIMOD (2015) International efforts to identify post-quake hazards. International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal. http://www.icimod.org/?q=18072. Accessed 23 June 2015

  • IFRC (2014) World disasters report 2014—focus on culture and risk. International Federation of Red Cross and Red Crescent Societies (IFRC), Geneva, Switzerland. http://www.ifrc.org/en/publications-and-reports/world-disasters-report/world-disasters-report-2013/. Accessed on 24 June 2015

  • Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439. doi:10.1007/s10346-013-0391-7

    Article  Google Scholar 

  • Kayastha P, Dhital MR, Smedt FD (2012) Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Nat Hazards 63:479–498. doi:10.1007/s11069-012-0163-z

    Article  Google Scholar 

  • Kayastha P, Bijukchhen S, Dhital M, Smedt F (2013) GIS based landslide susceptibility mapping using a fuzzy logic approach: a case study from Ghurmi–Dhad Khola area, Eastern Nepal. J Geol Soc India 82(3):249–261

    Article  Google Scholar 

  • Khamehchiyan M, Abdolmaleki P, Rakei B (2011) Landslide susceptibility mapping using back propagation neural networks and logistic regression: the Sephidargole case study, Semnan, Iran. Geomech Geoeng 6(3):237–250

    Article  Google Scholar 

  • Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manag 34(2):223–232

    Article  Google Scholar 

  • Lee S, Lee ML (2006) Detecting landslide location using KOMPSAT 1 and its application to landslide-susceptibility mapping at the Gangneung area, Korea. Adv Space Res 38:2261–2271

    Article  Google Scholar 

  • Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50:847–855. doi:10.1007/s00254-006-0256-7

    Article  Google Scholar 

  • Li C, Ma T, Sun L, Li W, Zheng A (2012) Application and verification of a fractal approach to landslide susceptibility mapping. Nat Hazards 61(1):169–185

    Article  Google Scholar 

  • Malczewski J (2004) GIS-based land-use suitability analysis: a critical overview. Prog Plan 62:3–65

    Article  Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide- susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276

    Article  Google Scholar 

  • Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343. doi:10.1016/S0013-7952(03)00069-3

    Article  Google Scholar 

  • Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464. doi:10.1007/s12665-012-1842-5

    Article  Google Scholar 

  • Pellicani R, Westen CJV, Spilotro G (2013) Assessing landslide exposure in areas with limited landslide information. Landslides. doi:10.1007/s10346-013-0386-4

    Google Scholar 

  • Petley DN, Dunning SA, Rosser NJ (2005) The analysis of global landslide risk through the creation of a database of worldwide landslide fatalities. In: Hungr O, Fell R, Couture R, Eberhardt E (eds) Landslide risk management. AT Balkema, Amsterdam, pp 367–374

    Google Scholar 

  • Pontius RG Jr, Schneider L (2001) Land-use change model validation by a ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85(1–3):239–248

    Article  Google Scholar 

  • Poudyal CP, Chang C, Oh HJ, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61:1049–1064. doi:10.1007/s12665-009-0426-5

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2010) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34(3):216–235

    Article  Google Scholar 

  • Ramani SE, Pitchaimani K, Gnanamanickam VR (2011) GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis. J Mt Sci 8:505–517

    Article  Google Scholar 

  • Reis S, Yalcin A, Atasoy M, Nisanci R, Bayrak T, Erduran M, Sancar C, Ekercin S (2012) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey). Environ Earth Sci 66:2063–2073. doi:10.1007/s12665-011-1432-y

    Article  Google Scholar 

  • Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:231–281

    Article  Google Scholar 

  • Sabokbar HF, Roodposhti MS, Tazik E (2014) Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology 226:15–24

    Article  Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219

    Article  Google Scholar 

  • Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5:9899. doi:10.1038/srep09899

    Article  Google Scholar 

  • Shahabi H, Ahmad BB, Khezri S (2013) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arab J Geosci 6(10):3885–3907. doi:10.1007/s12517-012-0650-2

    Article  Google Scholar 

  • Sujatha E, Rajamanickam V (2011) Landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanaltaluk, India, using weighted similar choice fuzzy model. Nat Hazards 59(1):401–425

    Article  Google Scholar 

  • Tachikawa T, Hato M, Kaku M, Iwasaki A (2011) The characteristics of ASTER GDEM version 2, IGARSS. http://www.jspacesystems.or.jp/ersdac/GDEM/E/1.html. Accessed 12 Oct 2014

  • Talaei R (2014) Landslide susceptibility zonation mapping using logistic regression and its validation in Hashtchin Region, Northwest of Iran. J Geol Soc India 84:68–86

    Article  Google Scholar 

  • USGS (2014) Frequently asked questions about the landsat missions. Department of the Interior, U.S. Geological Survey (USGS). http://landsat.usgs.gov/band_designations_landsat_satellites.php. Accessed 13 Feb 2015

  • USGS (2015) USGS global visualization viewer. Department of the Interior, U.S. Geological Survey (USGS). http://glovis.usgs.gov/index.shtml. Accessed 10 Feb 2015

  • Wisner B, Blaikie P, Cannon T, Davis I (2004) At Risk: natural hazards, people’s vulnerability and disasters, 1st edn. Routledge, New York

    Google Scholar 

  • Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116(3):274–283

    Article  Google Scholar 

  • Yilmaz C, Topal T, Süzen M (2012) GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environ Earth Sci 65(7):2161–2178

    Article  Google Scholar 

  • Zhang X, Yang F (2004). RClimDex (1.0): User manual. Climate Research Branch, Environment Canada, Downsview, Ontario, Canada

Download references

Acknowledgments

Bayes Ahmed is a Commonwealth Scholar funded by the UK govt. The author would like to thank the UCL Institute for Risk and Disaster Reduction (IRDR) for partially funding the fieldwork in Cox’s Bazar Municipality (CBM), Bangladesh. The author is grateful to Professor Dr. David E. Alexander, Renato Forte, the local people, the relevant officials and experts from CBM. My special gratitude goes to the research assistants (Abdullah Al Nyem, Sourav Biswas, Kawser Uddin, Md. Ahsan Habib, Shorojit Biswas, Shoukat Ahmed, and K.M. Risaduzzaman) from the Department of Urban and Regional Planning, Chittagong University of Engineering and Technology (CUET), Bangladesh. Finally, the author wants to thank the editors and the reviewers of ‘Natural Hazards’ journal for their constructive comments that improved the quality of this manuscript.

Author information

Authors and Affiliations

  1. Institute for Risk and Disaster Reduction, Department of Earth Sciences, University College London (UCL), Gower Street, London, WC1E 6BT, UK

    Bayes Ahmed

Authors
  1. Bayes Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bayes Ahmed.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmed, B. Landslide susceptibility modelling applying user-defined weighting and data-driven statistical techniques in Cox’s Bazar Municipality, Bangladesh. Nat Hazards 79, 1707–1737 (2015). https://doi.org/10.1007/s11069-015-1922-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-015-1922-4

Keywords

Search

Navigation