Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Landslides are identified as one of the most important natural hazards in many areas throughout the world. The essential purpose of this study is to compare general linear model (GLM), general additive model (GAM), multivariate adaptive regression spline (MARS), and modified analytical hierarchy process (M-AHP) models and assessment of their performances for landslide susceptibility modeling in the west of Mazandaran Province, Iran. First, landslides were identified by interpreting aerial photographs, and extensive field works. In total, 153 landslides were identified in the study area. Among these, 105 landslides were randomly selected as training data (i.e. used in the models training) and the remaining 48 (30 %) cases were used for the validation (i.e. used in the models validation). Afterward, based on a deep literature review on 220 scientific papers (period between 2005 and 2012), eleven conditioning factors including lithology, land use, distance from rivers, distance from roads, distance from faults, slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, and profile curvature were selected. The Certainty Factor (CF) model was used for managing uncertainty in rule-based systems and evaluation of the correlation between the dependent (landslides) and independent variables. Finally, the landslide susceptibility zonation was produced using GLM, GAM, MARS, and M-AHP models. For evaluation of the models, the area under the curve (AUC) method was used and both success and prediction rate curves were calculated. The evaluation of models for GLM, GAM, and MARS showed 90.50, 88.90, and 82.10 % for training data and 77.52, 70.49, and 78.17 % for validation data, respectively. Furthermore, The AUC value of the produced landslide susceptibility map using M-AHP showed a training value of 77.82 % and validation value of 82.77 % accuracy. Based on the overall assessments, the proposed approaches showed reasonable results for landslide susceptibility mapping in the study area. Moreover, results obtained showed that the M-AHP model performed slightly better than the MARS, GLM, and GAM models in prediction. These algorithms can be very useful for landslide susceptibility and hazard mapping and land use planning in regional scale.

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

Similar content being viewed by others

References

  • Abeare SM (2009) Comparisons of boosted regression tree, GLM, and GAM performance in the standardization of yellowfin tuna catch-rate data from the Gulf of Mexico longline fishery. Master’s Thesis. Louisiana State University

  • Agresti A (2002) Categorical data analysis, 2nd edn. Wiley, Hoboken 2002

    Book  Google Scholar 

  • Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput & Geosci 38:23–34

    Article  Google Scholar 

  • Alkhasawneh MS, Tay LT, Ngah UK, Al-batah MS, Isa NAM (2014) Intelligent landslide system based on discriminant analysis and cascade-forward back-propagation network. Arab J Sci Eng. doi:10.1007/s13369-014-1105-8

    Google Scholar 

  • Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput & Geosci 44:120–135

    Article  Google Scholar 

  • Avdac D, Poyraz M, Nefeslioglu HA, Sezer EA, Toptas TE, Celik D, Orhun K, Osna T, Ak S, Gokceoglu C (2014) Modified analytical hierarchy process (M-AHP) based river-line flood hazard assessment module running on GIS: Netcad architect environment. Geophysical Res Abstracts 16:2348

    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

    Article  Google Scholar 

  • Balashi MS, Mcguire AD, Duffy P, Flannigan M, Walsh J, Mellilo J (2009) Assessing the response of area burned to changing climate in western boreal North America using a multivariate adaptive regression splines (MARS) approach. Glob Chang Biol 15:578–600

    Article  Google Scholar 

  • Banuelas R, Antony J (2004) Modified analytic hierarchy process to incorporate uncertainty and managerial aspects. Int J Prod Res 42(18):3851–3872

    Article  Google Scholar 

  • Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kralˇovany–Liptovsky’ Mikuláš railway case study. Phys & Chem Earth 35:162–171

    Article  Google Scholar 

  • Beven KJ (1997) TOPMODEL: A critique. Hydrol Process 11:1069–1086

    Article  Google Scholar 

  • Beven KJ, Freer J (2001) A dynamic TOPMODEL. Hydrol Process 15:1993–2011

    Article  Google Scholar 

  • Beven K, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69

    Article  Google Scholar 

  • Binaghi E, Luzi L, Madella P, Pergalani F, Rampini A (2008) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster–Shafer approaches. Nat Hazards 17:77–97

    Article  Google Scholar 

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth

  • Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazard Earth Syst 5:853–862

    Article  Google Scholar 

  • Brenning A (2008) Statistical geocomputing combining R and SAGA: the example of landslide susceptibility analysis with generalized additive models. In: Bohner, J., Blaschke, T., Montanarella, L. (Eds.), SAGA – Seconds Out, vol. 19. Hamburger Beitrnge zur Physischen Geographie und Landschaftsokologie, pp. 23–32

  • Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides. doi:10.1007/s10346-014-0550-5

    Google Scholar 

  • Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13:2815–2831

    Article  Google Scholar 

  • Chen W, Chai H, Sun X, Wang Q, Ding X, Hong H (2016a) A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping. Arab J Geosci 9(3):1–16

    Article  Google Scholar 

  • Chen W, Chai H, Zhao Z, Wang Q, Hong H (2016b) Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China. Environ Earth Sci 75(6):1–13

    Google Scholar 

  • Chen W, Ding X, Zhao R, Shi S (2016c) Application of frequency ratio and weights of evidence models in landslide susceptibility mapping for the Shangzhou District of Shangluo City, China. Environ Earth Sci 75(1):1–10

    Article  Google Scholar 

  • Chen W, Li W, Chai H, Hou E, Li X, Ding X (2016d) GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ Earth Sci 75(1):1–14

    Article  Google Scholar 

  • Chung CF, Fabbri AG (1993) The representation of geoscience information for data integration. Non-renew Resour 2(2):122–139

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy. Catena 113(1):236–250

    Article  Google Scholar 

  • Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gutierrez AG, Rotigliano E, Agnesi V (2014) Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Belice River basin (western Sicily, Italy). Geomorphology. doi:10.1016/j.geomorph.2014.09.020

    Google Scholar 

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania. Environ Earth Sci 63:397–406

    Article  Google Scholar 

  • Craven P, Wahba G (1979) Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation. Numer Math 31:377–403

    Article  Google Scholar 

  • CRED (2009) Centre for Research on the Epidemiology of Disasters (CREM) website. http://www.dmdat.be/

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

    Article  Google Scholar 

  • De Boor C (1978) A practical guide to splines. Appl Math Sci 27:348

    Google Scholar 

  • Dehnavi A, Nasiri Aghdam I, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena 135:122–148

    Article  Google Scholar 

  • Donati L, 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 

  • Dou J, Yamagishi H, Pourghasemi HR, Yunus AP, Song X, Xu Y, Zhu Z (2015) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78(3):1749–1776

    Article  Google Scholar 

  • Egan JP (1975) Signal detection theory and ROC analysis. NY: Acad 195:266–268

    Google Scholar 

  • Eker AM, Dikmen M, Cambazoğlu S, Düzgün SHB, Akgün H (2014) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the ulus district, Bartın, northern Turkey. Int J Geogr Info Sci. doi:10.1080/13658816.2014.953164

    Google Scholar 

  • 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(6):720–730

    Article  Google Scholar 

  • Fang X (2008) Generalized additive models with correlated data, ProQuest, ISBN: 0549950907, 9780549950905, P 137

  • Feizizadeh B, Jankowski P, Blaschke T (2014) A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Comput & Geosci 64:81–95

    Article  Google Scholar 

  • Felicísimo AM, Gómez-Muñoz A (2004) GIS and predictive modelling: a comparison of methods applied to forestal management and decision-making. In: Geographical Information Systems Research UK. Proceedings of the GIS Research UK 12th Annual Conference: 143–144. Norwich

  • Felicísimo A, Cuartero A, Remondo J, Quirós E (2012) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides. doi:10.1007/s10346-012-0320

    Google Scholar 

  • Friedl B, Hölbling D, Eisank C, Blaschke T (2015) Object-based landslide detection in different geographic regions Geophysical Research Abstracts, Vol. 17, EGU2015–774, 2015, EGU General Assembly 2015

  • Friedman JH (1991) Multivariate adaptive regression splines. Ann Statist 19(1):1–67

    Article  Google Scholar 

  • Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129:376–386

    Article  Google Scholar 

  • Goetz JN, Guthrie RH, Brenning A (2014) Forest harvesting is associated with increased landslide activity during an extreme rainstorm on Vancouver Island, Canada. Nat Hazards Earth Syst Sci Discuss 2:5525–5574

    Article  Google Scholar 

  • Gokceoglu C, Nefeslioglu HA, Tanyildiz N (2015) A decision support system suggestion for the optimum railway route selection. G. Lollino et al. (eds.). Engineering Geology for Society and Territory 5:331–334. doi:10.1007/978-3-319-09048-1-63

    Google Scholar 

  • Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River basin, Venezuela. Eng Geol 78:11–27

    Article  Google Scholar 

  • Gorsevski PV, Gessler PE, Boll J, Elliot WJ, Foltz RB (2006) Spatially and temporally distributed modeling of landslide susceptibility. Geomorphology 80:178–198

    Article  Google Scholar 

  • Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89–100

    Article  Google Scholar 

  • Gutiérrez AG, Schnabel S, Contador JFL (2009) Using and comparing two non-parametric methods (CART and MARS) to model the potential distribution of gullies. Ecol Model 220:3630–3637

    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 

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

    Article  Google Scholar 

  • Haghiri M (2012) Applied nonparametric regression analysis: the choice of generalized additive models. Review of Economics & Finance 3:25–34

    Google Scholar 

  • Hasekiogullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63:1157–1179

    Article  Google Scholar 

  • Hastie TJ, Tibshirani RJ (1990) Generalized additive models. Chapman & Hall

  • Huang J, Zhou Q, Wang F (2015) Mapping the landslide susceptibility in Lantau Island, Hong Kong, by frequency ratio and logistic regression model. Annals GIS. doi:10.1080/19475683.2014.992373

    Google Scholar 

  • Iranian Landslide working party (ILWP), 2007. Iranian landslides list. Forest, Rangeland and Watershed Association, Iran, p. 60.

  • Jebur MN, Pradhan B, Shafapour Tehrany M (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165

    Article  Google Scholar 

  • Jia G, Tian Y, Liu Y, Zhang Y (2008) A static and dynamic factors-coupled forecasting model of regional rainfall-induced landslides: a case study of Shenzhen. Sci China Ser E 51:164–175

    Article  Google Scholar 

  • Kavzoglu T, Sahin E, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439

    Article  Google Scholar 

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in peri-alpine Slovenia. Geomorphology 74:17–28

    Article  Google Scholar 

  • Lan HX, Zhou CH, Wang LJ, Zhang HY, Li RH (2004) Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China. Eng Geol 76:109–128

    Article  Google Scholar 

  • Leathwick JR, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199:88–196

    Article  Google Scholar 

  • Lee S (2007) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52:615–623

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lee S, Hwang J, Park I (2013) Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena 100:15–30

    Article  Google Scholar 

  • Lee MJ, Park I, Lee S (2015) Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea. Environ Earth Sci. doi:10.1007/s12665-015-4048-9

    Google Scholar 

  • Lehmann A, McC Overton J, Leathwick JR (2002) GRASP: generalized regression analysis and spatial prediction. Ecol Model 157:189–207

    Article  Google Scholar 

  • Marimon M, Hjort J, Thuiller W, Luoto M (2008) A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland. Earth Surf Process Landf 33:2241–2254

    Article  Google Scholar 

  • Maunder MN, Punt AE (2004) Standardizing catch and effort data: a review of recent approaches. Fish Res 70:141–159

    Article  Google Scholar 

  • Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, Kanevski M (2014) Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci 46:33–57

    Article  Google Scholar 

  • Milborrow S (2009) Derived from mda: mars by Trevor Hastie and Rob Tibshirani. earth: Multivariate Adaptive Regression Splines, 2009. R Package, http://CRAN.R-project.org/package=earth

  • Miner AS, Vamplew P, Windle DJ, Flentje P, Warner P (2010) A comparative study of various data mining techniques as applied to the modeling of landslide susceptibility on the Bellarine peninsula, Victoria, Australia. Geologically Active, Proceedings of the 11th IAEG Congress of the International Association of Engineering Geology and the Environment, Auckland

    Google Scholar 

  • Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of evidence models. J Asian Earth Sci 61:221–236

    Article  Google Scholar 

  • Moisen GG, Frescino Tracey S (2002) Comparing five modelling techniques for predicting forest characteristics. Ecol Model 157:209–225

    Article  Google Scholar 

  • Morgan JN, Sonquist JA (1963) Problems in the analysis of survey data, and a proposal. J Am Stat Assoc 58:415–434

    Article  Google Scholar 

  • Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3:159–173

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR (2015) Water Resour Manage 29: 5217. doi:10.1007/s11269-015-1114-8

  • Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20

  • Nasiri Aghdam I, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran. Environ Earth Sci 75(7):1–20

    Google Scholar 

  • 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, pp 10

  • Nefeslioglu HA, Sezer EA, Gokceoglu C, Ayasd Z (2013) A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Comput & Geosci 59:1–8

    Article  Google Scholar 

  • Nelder JA, Wedderburn RWM (1972) Generalized linear models. J R Stat Soc A135:370–384

    Google Scholar 

  • Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2013) Landslide susceptibility mapping at Zonouz plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards. doi:10.1007/s11069-013-0932-3

    Google Scholar 

  • O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual & Quant 41(5):673–690

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ohlmacher GC (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Engr Geol 91:117–134

    Article  Google Scholar 

  • Osna T, Sezer EA, Akgun A (2014) GeoFIS: an integrated tool for the assessment of landslide susceptibility. Comput & Geosci 66:20–30

    Article  Google Scholar 

  • Ozdemir A (2011a) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405:123–136

    Article  Google Scholar 

  • Ozdemir A (2011b) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405:123–136

    Article  Google Scholar 

  • Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197

    Article  Google Scholar 

  • Park NW, Chi KH (2008) Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model. Int J Remote Sens 29:247–264

    Article  Google Scholar 

  • Park I, Lee S (2014) Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area. Korea, Int J Remote Sens. doi:10.1080/01431161.2014.943326

    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

    Article  Google Scholar 

  • Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the three gorges area, China. Geomorphology 204:287–301

    Article  Google Scholar 

  • Petschko H, Brenning A, Bell R, Goetz J, Glade T (2014) Assessing the quality of landslide susceptibility maps – case study Lower Austria. Nat Hazards Earth Syst Sci 14:95–118

    Article  Google Scholar 

  • Pham BT, Tien BD, Pourghasemi HR, Indra P, Dholakia MB (2015) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol. doi:10.1007/s00704-015-1702-9

    Google Scholar 

  • Polykretis C, Ferentinou M, Chalkias C (2014) A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bull Eng Geol Environ. doi:10.1007/s10064-014-0607-7

    Google Scholar 

  • Pourghasemi HR (2014) Landslide hazard prediction using data mining methods in the North of Tehran City. Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (Ph.D) in Watershed Management Engineering and Sciences, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University. 143PP (In Persian)

  • Pourghasemi HR, Beheshtirad M (2015) Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang watershed, Iran. Geocarto Int 30(6):662–685

    Article  Google Scholar 

  • Pourghasemi HR, Kerle N (2016) Random Forest-evidential belief function based landslide susceptibility assessment in western Mazandaran Province, Iran. Environ Earth Sci 75:185. doi:10.1007/s12665-015-4950-1

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012a) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69:749–779

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2014a) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (north of Tehran, Iran. Arab J Geosci 7(5):1857–1878

    Article  Google Scholar 

  • Pourghasemi HR, Beheshtirad M, Pradhan B (2014b) A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models in Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards and Risk. doi:10.1080/19475705.2014.984247

    Google Scholar 

  • Pourtaghi Z, Pourghasemi HR, Rossi M (2015) Forest fire susceptibility mapping in the Minudasht forests, Golestan Province, Iran. Environ Earth Sci 73:1515–1533

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput & Geosci 51:350–365

    Article  Google Scholar 

  • R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna ISBN 3-900051-07-0, URL http://www.R-project.org

    Google Scholar 

  • Regmi AD, Yoshida K, Pourghasemi HR, Dhital MR, Pradhan B (2014) Landslide susceptibility mapping along Bhalubang–Shiwapur area of mid-western Nepal using frequency ratio and conditional probability models. J Mount Sci 11(5):1266–1285

    Article  Google Scholar 

  • Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281

    Article  Google Scholar 

  • Saaty TL (1980) The analytical hierarchy process. McGraw-Hill, New York

    Google Scholar 

  • Schuster R (1996) Socioeconomic significance of landslides. In: Turner AK, Schuster RL (eds) Landslides: investigation and mitigation, Transportation Research Board, National Research Council, special report, 247. National Academic Press, Washington, DC, pp. 12–36

    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, Ahmad BB (2015) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin. Iran Environ Earth Sci. doi:10.1007/s12665-015-4028-0

    Google Scholar 

  • Sharma LP, Nilanchal P, Ghose MK, Debnath P (2013) Synergistic application of fuzzy logic and geo-informatics for landslide vulnerability zonation-a case study in Sikkim Himalayas, India. Appl Geomat 5:271–284

    Article  Google Scholar 

  • Shirzadi A, Lee S, HJ O, Chapi K (2012) A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Nat Hazards 64:1639–1656

    Article  Google Scholar 

  • Shortliffe E, Buchanan B (1975) A model of inexact reasoning in medicine. Mathematical Biosciences, 23:351–379.

  • Siyahghalati S, Saraf AK, Pradhan B, Jebur MN, Shafapour Tehrany M (2014) Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images. Geomatics, Nat Hazards & Risk. doi:10.1080/19475705.2014.898702

  • Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using random forests. Remote Sens Environ 115:2564–2577

    Article  Google Scholar 

  • Süzen ML, Doyuran V (2004) 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 

  • Talebi A, Uijlenhoet R, Troch PA (2007) Soil moisture storage and hillslope stability. Nat Hazards Earth Syst Sci 7:523–534

    Article  Google Scholar 

  • Tassetti N, Bernardini A, Malinverni ES (2008) Use of remote sensing data and GIS technology for assessment of landslide hazards in Susa valley, Italy. EARSeL eProceedings 7:59–67

    Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides. doi:10.1007/s10346-015-0557-6

    Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS based neuro fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput & Geosci 36(9):1101–1114

    Article  Google Scholar 

  • Van Westen CJ (1994) GIS in landslide hazard zonation: a review, with examples from the Andes of Colombia. In: Price M, Heywood I (eds) Mountain environments and geographic information systems. Taylor & Francis, Basingstoke, pp. 135–165

    Google Scholar 

  • Vargas LG (1990) An overview of the analytic hierarchy process and its applications. Eur J Oper Res 48:2–8

    Article  Google Scholar 

  • Venables WN, Dichmont CM (2004) A generalized linear model for catch allocation: an example of Australia’s Northern Prawn Fishery. Fish Res 70: 405–422.

  • Venables WN, Ripley BD (2002) Modern applied statistics with S, Fourth edn. Springer, New York ISBN 0-387-95457-0

  • Vorpahl P, Elsenbeer H, Marker M, Schröder B (2012) How can statistical models help to determine driving factors of landslides? Ecol Model 239:27–39

    Article  Google Scholar 

  • Wang YH, Raulier F, Ung CH (2005) Evaluation of spatial predictions of site index obtained by parametric and nonparametric methods- a case study of Lodgepole pine productivity. For Ecol Manag 214:201–211

    Article  Google Scholar 

  • Xu C, Dai F, Xu X, Lee YH (2012a) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145:70–80

    Article  Google Scholar 

  • Xu C, Xu XW, Dai FC, Saraf AK (2012b) Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Comput & Geosci 46:317–329

    Article  Google Scholar 

  • Xu C, Xu X, Dai F, Wu Z, He H, Shi F, Wu X, Xu S (2013) Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the may 12, 2008 Wenchuan earthquake of China. Nat Hazards 68:1–18

    Article  Google Scholar 

  • Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72:1–12

    Article  Google Scholar 

  • Yalcin A, Reis S, Cagdasoglu A, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85:274–287

    Article  Google Scholar 

  • Yeon Y-K, Han J-G, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116:274–283

    Article  Google Scholar 

  • Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, p 423.

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector. Environ Earth Sci 61(4):821–836

    Article  Google Scholar 

  • Youssef AM (2015) Landslide susceptibility delineation in the Ar-Rayth area, Jizan, Kingdom of Saudi Arabia, using analytical hierarchy process, frequency ratio, and logistic regression models. Environ Earth Sci. doi:10.1007/s12665-014-4008-9

    Google Scholar 

  • Youssef AM, Pradhan B, Jebur MN, El-Harbi HM (2014a) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area. Saudi Arabia Environ Earth Sci. doi:10.1007/s12665-014-3661-3

    Google Scholar 

  • Youssef AM, Al-kathery M, Pradhan B (2014b) Landslide susceptibility mapping at Al-hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J. doi:10.1007/s12303-014-0032-8

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  2. CNR IRPI, via Madonna Alta 126, 06128, Perugia, Italy

    Mauro Rossi

  3. Earth Science Department, University of Perugia, Perugia, Italy

    Mauro Rossi

Authors
  1. Hamid Reza Pourghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mauro Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Reza Pourghasemi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pourghasemi, H.R., Rossi, M. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol 130, 609–633 (2017). https://doi.org/10.1007/s00704-016-1919-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-016-1919-2

Keywords

Search

Navigation