Skip to main content

Advertisement

SpringerLink
Log in

Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin)

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Landslides are among the great destructive factors which cause lots of fatalities and financial losses all over the world every year. Studying of the factors affecting occurrence of landslides in a region and zoning the resulting damages will certainly play a crucial role in mitigating such phenomena. In this research, through geological maps and field studies, we primarily prepared a map for landslide distributions in Zab basin—an area of 520 km2 in the southwest mountainsides of West Azerbaijan Province. By applying other source of information such as the existing thematic maps, we studied and defined the factors (slope, slope aspect, distance to road, distance to drainage network, distance to fault, land use and land cover, geological factors, horizontal gravity acceleration of earthquakes, and climatic condition of the studied area) that affect occurrence of the landslides. To get better precision and higher speed and facility in our analysis, all descriptive and spatial information were entered into geographic information system (GIS) system and Ilwis software. We also used Satellite images (Landsat ETM+ and SPOT 5), producing land cover and landslide-inventory maps, respectively. After preparation of the influential parameters on landslides, we drew the zoning maps of slide hazard via four different statistical methods and then evaluated and compared them. By analyzing the obtained index and by comparing landslide distribution map and zoning map of landslide susceptibility prepared by each of the methods in GIS environment, we found that bivariate method of information value analysis, bivariate method of density-area, multivariate method with linear regression analysis, and multivariate method of discriminate analysis take priority, respectively. Finally, as this research shows, despite their simplicity, bivariate statistical methods have more acceptable precision than multivariate methods, and consequently, they are more compatible with landslide susceptibility of the region. From the results, lithology, slope, annual rainfall, land cover, slope aspect, distance to waterway, distance to road, horizontal gravity acceleration, and distance to fault are very influential to landslides in the region.

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

Similar content being viewed by others

References

  • AGS (2007) Guideline for landslide susceptibility, hazard and risk zoning for land use management. Aust Geomech Soc 42(1):45–57

    Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44

    Article  Google Scholar 

  • Ambersiz N, Melvil HB (2009) History of Iran's earthquakes. Trans. Radeh, Agah Press Institute, Tehran

    Google Scholar 

  • Ardizzone F, Cardinali M, Carrara A, Guzzetti F, Reichenbach P (2002) Uncertainty and errors in landslide mapping and landslide hazard assessment. Natu Haz and Ear Sys Sci J 2:3–14

    Article  Google Scholar 

  • Atkinson PM, Massari R (1998) A generalized linear modeling of susceptibility to landsliding in the Central Apennines, Italy. Comp Geosci 24(4):373–385

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in Kakuda-Yahiko Mountains, central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geo 81:432–445

    Article  Google Scholar 

  • Bai SB, Wang J, Lu GN, Zhou PG, Hou SS, Xu SN (2009) GIS based and data driven bivariate landslide-susceptibility mapping in the Three Gorges Area, China. Pedosphere 19(1):14–20

    Article  Google Scholar 

  • Barzegar F, Maleki A (1998) Relative earthquake in Iran. Geo-structure Department and Relative Estimation of Earthquake, Studies and Researches Center for urbanism and Architecture of Iran, p 81-100

  • Begueria S (2006) Changes in land cover and shallow landslide activity: a case study in the Spanish Pyrenees. Geomorphology 74:196–206

    Article  Google Scholar 

  • Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geo 15(3):403–427

    Article  Google Scholar 

  • Carrara A (1988) Landslide hazard mapping by statistical method: a “black box” approach. In: Proceedings of Workshop on Natural Disaster in European Mediterranean Countries. Conzilio Nazionale delle Richerche, Perugia, Italy, 205–224

  • Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1990) Geographical information systems and multivariate models in landslide hazard evaluation. In: ALPS 90 (Alpine Landslide Practical Seminar) Proceedings of the 6th International Conference and Field Workshop on Landslide. Universita degli studi di Milano, Italy, August 31–September 12, 1990

  • Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Ear Sur Proc and Land 16:427–445

    Article  Google Scholar 

  • Carrara A, Cardinali M, Guzzetti F (1995) GIS technology in mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer Academic Publishers, Dordrecht, pp 135–175

    Chapter  Google Scholar 

  • Castellanos AEA, Van Westen CJ (2007) Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation. Landslides 4(4):311–325

    Article  Google Scholar 

  • Che VB, Kervyn M, Ernst GGJ, Trefois P, Ayonghe S, Jacobs P, Van Ranst E, Suh CE (2011) Systematic documentation of landslide events in Limbe area (Mt Cameroon Volcano, SW Cameroon): geometry, controlling and triggering factors. Nat Haz 59:47–74

    Article  Google Scholar 

  • Chung CF, Fabbri AG, van Westen CJ (1995) Multivariate regression analyses for landslide hazard zonation. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer Academic Publishers, Netherlands, pp 107–134

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  • Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Appennines). Environ Geol 50(7):941–961

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(34):213–228

    Article  Google Scholar 

  • Dai FC, Xu C, Yao X, Xu L, Tu XB, Gong QM (2010) Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake. J of Asi Earth Scie 40(3):883–895. doi:10.1016/j.jseaes.2010.04.010

    Google Scholar 

  • Dewitte O, Chung C, Cornet Y, Daoudi M, Demoulin A (2010) Combining spatial data in landslide reactivation susceptibility mapping: a likelihood ratio-based approach in W Belgium. Geomorphology 122:153–166

    Article  Google Scholar 

  • Dymond JR, Auseeil AG, Shepherd JD, Buettner L (2006) Validation of a region-wide model of landslide susceptibility in the Manawatu-Wanganui Region of New Zealand. Geomorphology 74:70–79

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C, Van Asch WJ (2004) Landslide susceptibility zoning of North of Yenice (NW Turkey) by multivariate statistical techniques. Nat Haz 32:1–23

    Article  Google Scholar 

  • Greif V, Sassa K, Fukuoka H (2006) Failure mechanism in an extremely slow rock slide at Bitchu-Matsuyama castle site (Japan). Landslides 3(1):22–38

    Article  Google Scholar 

  • Gruber S, Huggle C, Pike R (2009) Modeling mass movements and landslide susceptibility. Deve in Soi Sci 33:69–75

    Google Scholar 

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

    Article  Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66

    Article  Google Scholar 

  • Iovine G (2008) Mud-flow and lava-flow susceptibility and hazard mapping through numerical modelling, GIS techniques, historical and geoenvironmental analyses. In: Sanchez-Marre M, Bejar J, Comas J, Rizzoli AE, Guariso G (Eds.), Proceedings of the iEMSs Fourth Biennial Meeting: International Congress on Environmental Modelling and Software (iEMSs 2008), vol. 3. International Environmental Modelling and Software Society, Barcelona, Catalonia, July 2008. pp. 1447–1460. ISBN: 978-84-7653-074-0

  • Iovine G, Di Gregorio S, Lupiano V (2003a) Assessing debris-flow susceptibility through cellular automata modelling: an example from the May 1998 disaster at Pizzo d’Alvano (Campania, southern Italy). In: Rickenmann D, Chen CL (Eds.), Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment. Proc. 3rd DFHM Int. Conference, Davos, Switzerland, vol. 1. Millpress Science Publishers, Rotterdam, 623–634

  • Iovine G, Di Gregorio S, Lupiano V (2003b) Debris-flow susceptibility assessment through cellular automata modeling: an example from 15–16 December 1999 disaster at Cervinara and San Martino Valle Caudina (Campania, southern Italy). Nat Hazards Earth Sys Scie 3:457–468

    Article  Google Scholar 

  • Khezri S (2010) Discovery of the “Tuzhal” cave from geomorphological aspect. The 2nd International Geography Symposium GEOMED 2010, Turkey: Kemer

  • 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

    Article  Google Scholar 

  • 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 4:327–338

    Article  Google Scholar 

  • Liu YS, Wang JY, Guo LY (2006) GIS based assessment of land suitability for optimal allocation in the Qinling Mountains, China. Pedosphere 16(5):579–586

    Article  Google Scholar 

  • Nandi A, Shakoor A (2009) A GIS based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20

    Article  Google Scholar 

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural network with different sampling strategies for the preparation maps of landslide susceptibility. Eng Geol 97:78–89

    Article  Google Scholar 

  • Oh HJ, Lee S, Chotikasathien W, Kim CH, Kwon JH (2009) Predictive landslide susceptibility mapping using spatial information in the Pechabunarea of Thailand. Environ Geol 57:641–651

    Article  Google Scholar 

  • Oh HJ, Kim YS, Choi JK, Lee S (2011) GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol 399:158–172

    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

    Article  Google Scholar 

  • Paliwal M, Kumar UA (2009) Neural networks and statistical techniques: a review of applications. Expert Syst Appl 36:2–17

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2010) Remote sensing and GIS based landslide susceptibility analysis and its cross validation in three test areas using a frequency ratio model. Photogram Ferne Geoinfo 1:17–32

    Article  Google Scholar 

  • Pranab Kumar S, Anderson TW, Arnold SF, Eaton ML, Giri NC, Gnanadesikan R, Kendall MG (1986) Contemporary textbooks on multivariate statistical analysis: a panoramic appraisal and critique". J of the Amer Statis Associ 81(394):560–564

    Article  Google Scholar 

  • Schervish MJ (1987) A review of multivariate analysis. Statis Sci 2(4):396–413. doi:10.1214/ss/1177013111

    Article  Google Scholar 

  • Soeters R, van Westen CJ (1996) Slope instability recognition analysis and zonation. In Turner KT, Schuster RL (eds.) Landslides: investigation and mitigation. Transportation Research Board National Research Council, Special Report, Washington, D. C. 247: 129–177

  • Song RH, Hiromu D, Kazutok A, Usio K, Sumio M (2008) Modeling the potential distribution of shallow-seated landslides using the weight of evidence method and logistic regression model: a case study of the Sabae Area, Japan. Inter J of Sedim Rese 23:106–118

    Article  Google Scholar 

  • Sorriso Valvo M (2002) Landslides from inventory to risk. In: Rybar J, Stemberk J, Wagner P (eds) Landslides proceedings of the international conference on landslides. Balkema, Rotterdam, pp 79–93

    Google Scholar 

  • Suezen ML, Doyuran V (2004b) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71:303–321

    Article  Google Scholar 

  • Suzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45(5):665–679

    Article  Google Scholar 

  • Thiery Y, Puissant A, Malet JP, Remaitre A, Beck E, Sterlacchini S, Maquaire O (2003) Towards the construction of a spatial database to manage landslides with GIS in mountainous environment. Proceedings of AGILE 2003: The Science behind the infrastructure, 6th AGILE Conference on Geographic Information Science. 24th–26th, April 2003, Lyon, France, 37–44

  • Thiery Y, Sterlacchini S, Malet JP, Puissant A, Maquaire O (2004) Strategy to reduce subjectivity in landslide susceptibility zonation by GIS in complex mountainous environments. In: Toppen F, Prastacos P (Eds.), Proceedings of AGILE 2004: 7th AGILE Conference on Geographic Information Science. 29th April–1st May 2004, Heraklion, Greece, 623–634

  • Van Westen CJ, Rangers NM, Terlien T, Soeters R (1999) Prediction of the occurrence of slope instability phenomena through GIS based hazard zonation. Geologi Runds 86:404–414

    Article  Google Scholar 

  • Van Westen CJ, Rangers NM, Terlien T, Soeters R (2002) Prediction of the occurrence of slope instability phenomena through GIS based hazard zonation. Instability Zonation, part 1: Exercise, ITC, and Publication Number 15

  • Van Westen CJ, van Asch ThWJ, Soeters R (2005) Landslide hazard and risk zonation: why is it still so difficult? In: Bulletin of Engineering Geology and the Environment IAEG, 65(2):167-184

    Google Scholar 

  • Vijith H, Rejith PG, Madhu G (2009) Using Info Val Method and GIS techniques for the spatial modelling of landslides susceptibility in the Upper catchment of River Meenachil in Kerala. Indian Soc of Remote Sens 37:241–250

    Article  Google Scholar 

  • Wills CJ, McCrinck TP (2002) Comparing landslide inventories: the map depends on the method. Environ Engin Geos 8:279–293

    Article  Google Scholar 

  • Yalcin A, Bulut F (2007) Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41:201–226

    Article  Google Scholar 

  • Yesilnacara E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in mediumscale study, Hendek Region (Turkey). Engin Geol 79:251–266

    Article  Google Scholar 

  • Yin KL, Yan TZ (1988) Statistical prediction model for slope instability of metamorphosed rocks. In: Bonnard C (Ed.), Landslides, Proceedings of Fifth International Symposium in Landslides. Balkema, Rotterdam, 1269–1272

Download references

Acknowledgments

We are thankful to the Department of Remote Sensing in Universiti Teknologi Malaysia and Department of Physical Geography in University of Kurdistan and UTM International Doctorial Fellowship (IDF) for providing the facilities for this investigation.

Author information

Authors and Affiliations

  1. Department of Remote Sensing, Faculty of Geo Information and Real estate, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Johor, Malaysia

    H. Shahabi & B. B. Ahmad

  2. Department of Physical Geography, University of Kurdistan, Kurdistan, Iran

    S. Khezri

Authors
  1. H. Shahabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. B. Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Khezri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. B. Ahmad.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12517-012-0650-2

Keywords

Search

Navigation