GISbased landslide susceptibility mapping with probabilistic likelihood ratio and spatial multicriteria evaluation models (North of Tehran, Iran)
 H. R. Pourghasemi,
 H. R. Moradi,
 S. M. Fatemi Aghda,
 C. Gokceoglu,
 B. Pradhan
 … show all 5 hide
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Get AccessAbstract
The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multicriteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWISGIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multicriteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.
 Akgun, A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multicriteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9: pp. 93106
 Akgun, A, Sezer, EA, Nefeslioglu, HA, Gokceoglu, C, Pradhan, B (2012) An easytouse MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38: pp. 2334
 Akgun, A, Turk, N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multi criteria decision analysis. Environ Earth Sci 61: pp. 595611
 Aleotti, P, Chowdhury, R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58: pp. 2144
 Althuwaynee, OF, Pradhan, B, Lee, S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44: pp. 120135
 Aniya, M (1985) Landslidesusceptibility mapping in the Amahata river basin, Japan. Annals Associ of American Geograph 75: pp. 102114
 Atkinson, PM, Massari, R (2011) Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy. Geomorphology 130: pp. 5564
 Ayalew, L, Yamagishi, H (2005) The application of GISbased logistic regression for landslide susceptibility mapping in the KakudaYahiko Mountains, Central Japan. Geomorphology 65: pp. 1531
 Ayalew, L, Yamagishi, H, Ugawa, N (2004) Landslide susceptibility mapping using GIS based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1: pp. 7381
 Ballabio, C, Sterlacchini, S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Math Geosci 44: pp. 4770
 Bednarik, M, Magulova, B, Matys, M, Marschalko, M (2010) Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulas railway case study. Phys Chem Earth Parts A/B/C 35: pp. 162171
 Beven, K, Kirkby, MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24: pp. 4369
 Binaghi, E, Luzi, L, Madella, P, Pergalani, F, Rampini, A (1998) Slope instability zonation: a comparison between certainty factor and Fuzzy Dempster–Shafer approaches. Nat Hazards 17: pp. 7797
 Boerboom L, Flacke J, Sharifi A, Alan O (2009) Webbased spatial multicriteria evaluation (SMCE) software, ITC Working paper 1, for the ForestClim Project 25 pp
 Castellanos, E, Westen, CJ (2007) Generation of a landslide risk index map for Cuba using spatial multicriteria evaluation. Landslide 4: pp. 311325
 Cevik, E, Topal, T (2003) GISbased landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44: pp. 949962
 Champati Ray, DP, Dimri, S, Lakhera, RC, Sati, S (2007) Fuzzybased method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4: pp. 101111
 Choi, J, Oh, HJ, Lee, HJ, Lee, C, Lee, S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124: pp. 1223
 Chung, CJ, Fabbri, AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30: pp. 451472
 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: pp. 397406
 Costanzo, D, Rotigliano, E, Irigaray, C, JimenezPervarez, JD, Chacon, J (2012) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain). Nat Hazards Earth Syst Sci 12: pp. 327340
 Dai, FC, Lee, CF (2001) Terrainbased mapping of landslide susceptibility using a geographical information system: a case study. Canadian Geotechl J38: pp. 911923
 Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2012) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at MuglingNarayanghat road section in Nepal Himalaya. Nat Hazards. doi:10.1007/s1106901203476
 Dietrich, EW, Reiss, R, Hsu, ML, Montgomery, DR (1995) A processbased model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrol Processes 9: pp. 383400
 Ercanoglu, M, Gokceoglu, C (2002) Assessment of landslide susceptibility for a landslideprone area (North of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41: pp. 720730
 Ercanoglu, M, Gokceoglu, C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75: pp. 229250
 Ercanoglu, M, Kasmer, O, Temiz, N (2008) Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping. Bull Eng Geol Environ 67: pp. 565578
 Ermini, L, Catani, F, Casagli, N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66: pp. 327343
 Felicisimo A, Cuartero A, Remondo J, Quiros 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/s1034601203201
 Geology Survey of Iran (GSI) (1997) http://www.gsi.ir/Main/Lang_en/index.html
 Gokceoglu, C, Sezer, EA Soft computing modeling in landslide susceptibility assessment. In: Pradhan, B, Buchroithner, M eds. (2012) Terrigenous mass movements. Springer, Berlin, pp. 5190
 Gokceoglu, C, Sonmez, H, Ercanoglu, M (2000) Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey. Eng Geol 55: pp. 277296
 Gomez, H, Kavzoglu, T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78: pp. 1127
 Gorsevski, PV, Jankowski, P (2008) Discrening landslide susceptibility using rough sets. Comput Environ Urban Syst 32: pp. 5365
 Gorsevski, PV, Jankowski, P, Paul, PE (2006) Heuristic approach for mapping landslide hazard integrating fuzzy logic with analytic hierarchy process. Control Cybern 35: pp. 126
 Guzzetti, F, Carrarra, A, Cardinali, M, Reichenbach, P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multiscale study, Central Italy. Geomorphology 31: pp. 81216
 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. doi:10.1007/s1106901202181
 He, S, Pan, P, Dai, L, Wang, H, Liu, J (2012) Application of kernelbased Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology 171–172: pp. 3041
 Hengl, T, Gruber, S, Shrestha, DP (2003) Digital terrain analysis in ILWIS. International Institute for GeoInformation Science and Earth Observation Enschede, The Netherlands
 Herwijnen, MV (1999) Spatial decision support for environmental management. Vrije Universiteit, Amsterdam
 Hizbaron DR, Baiquni M, Sartohadi J, Rijanta R, Coy M (2011) Assessing social vulnerability to seismic hazard through spatial multi criteria evaluation in Bantul District, Indonesia. Conference of Development on the Margin, Tropentag 2011, 4 pp
 Irigaray, C, Fernandez, T, Hamdouni, REI, Chacon, J (2007) Evaluation and validation of landslidesusceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Nat Hazards 41: pp. 6179
 I.R. of Iran Meteorological Org (IRIMO) (2011) http://www.irimo.ir/english
 Jenness J (2002) Surface Areas and Ratios from Elevation Grid, Jenness Enterprises, http://www.jennessent.com/arcview/ surface_areas.htm (connected: 10.08.2003)
 Juang, CH, Lee, DH, Sheu, C (1992) Mapping slope failure potential using fuzzy sets. J Geotech Eng Div ASCE 118: pp. 475493
 Kanungo, DP, Arora, MK, Sarkar, S, Gupta, RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85: pp. 347366
 Kincal, C, Akgun, A, Koca, MY (2009) Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci 59: pp. 745756
 Komac, M (2006) A landslide susceptibility model using analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74: pp. 1728
 Kritikos, T, Davies, TRH (2011) GISbased multicriteria decision analysis for landslide susceptibility mapping at northern Evia, Greece. Z dt Ges Geowiss 162: pp. 421434
 Lee, S (2004) Soil erosion assessment and its verification using the universal soil loss equation and geographic information system: a case study at Boun, Korea. Environ Geol 45: pp. 457465
 Lee S, Choi J, Oh H (2009) Landslide susceptibility mapping using a neurofuzzy. Abstract presented at American Geophysical Union, Fall Meeting 2009, abstract #NH53A1075
 Lee, S, Min, K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40: pp. 10951113
 Lee, S, Pradhan, B (2006) Probabilistic landslide risk mapping at Penang Island, Malaysia. J Earth Syst Sci 115: pp. 661672
 Lee, S, Pradhan, B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4: pp. 3341
 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: pp. 327338
 Lee, S, Talib, JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47: pp. 982990
 Li C, Ma T, Sun L, Li W, Zheng A (2011) Application and Verification of fractal approach to landslide susceptibility mapping. Natl Hazards. doi:10.1007/s110690119804x
 Looijen, JM (2010) EIA & SEA: Environmental Impact Assessment and Strategic Environmental Assessment using spatial decision support tools: distance education. ITC, Enschede
 Malczewski, J (1999) GIS and multi criteria decision analysis. Wiley, New York
 Marjanović, M, Kovačević, M, Bajat, B, Voženílek, V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123: pp. 225234
 Mathew, J, Jha, VK, Rawat, GS (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6: pp. 1726
 Melchiorre, C, Matteucci, M, Azzoni, A, Zanchi, A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94: pp. 379400
 Mohammady, M, Pourghasemi, HR, Pradhan, B (2012) Landslide susceptibility mapping at Golestan Province Iran: a comparison between frequency ratio, DempsterShafer, and weightsofevidence models. J Asian Earth Sci 61: pp. 221236
 Moore, ID, Burch, GJ (1986) Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Res 22: pp. 13501360
 Moore, ID, Grayson, RB, Ladson, AR (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydro Process 5: pp. 330
 Nafooti MH, Chabok Boldaje M(2011) Spatial prioritizing of pastures using spatial multi criteriaevaluation (Case study: Yoosef Abad watershed—Iran). 2011 2nd International Conference on Environmental Engineering and Applications IPCBEE vol. 17 (2011) IACSIT Press, Singapore, p. 4
 Nagarajan, R, Roy, A, Vinod Kumar, R, Mukherjee, A, Khire, MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Env 58: pp. 275287
 Nandi, A, Shakoor, A (2010) A GISbased landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110: pp. 1120
 Nefeslioglu, HA, Gokceoglu, C, Sonmez, H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97: pp. 171191
 Nefeslioglu, H.A., Sezer, E., Gökçeoğlu, C., Bozkır, A.S., Duman, T.Y (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering, 2010, Article ID: 901095
 Negnevitsky, M (2002) Artificial intelligence—a guide to intelligent systems. AddisonWesley Co, Great Britain
 Nilaweera, NS, Nutalaya, P (1999) Role of tree roots in slope stabilisation. Bull Eng Geol Environ 57: pp. 337342
 Oh, HJ, Lee, S (2010) Crossvalidation of logistic regression model for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Adv 3: pp. 4455
 Oh, HJ, Lee, S (2011) Crossapplication used to validate landslide susceptibility maps using a probabilistic model from Korea. Environ Earth Sci 64: pp. 395409
 Oh, HJ, Pradhan, B (2011) Application of a neurofuzzy model to landslidesusceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37: pp. 12641276
 Okimura, T, Kawatani, T Mapping of the potential surface—failure sites on granite slopes. In: Gardiner, E eds. (1987) International geomorphology 1986 part I. Wiley, Chichester, pp. 121138
 Ozdemir, A (2009) Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS. Environ Geol 57: pp. 16751686
 Pachauri, AK, Gupta, PV, Chander, R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36: pp. 325334
 Pachauri, AK, Pant, M (1992) Landslide hazard mapping based on geological attributes. Eng Geol 32: pp. 81100
 Parise, M (2001) Landslide mapping techniques and their use in the assessment of the landslide hazard. Phys Chem Earth 26: pp. 697703
 Piegari, E, Cataudella, V, Maio, R, Milano, L, Nicodemi, M, Soldovieri, MG (2009) Electrical resistivity tomography and statistical analysis in landslide modelling: a conceptual approach. J Appl Geophysics 68: pp. 151158
 Pielke, RA, Schellnhuber, HJ, Sahagian, D (2003) Nonlinearities in the earth system. Global Change News Lett 55: pp. 1115
 Pourghasemi HR (2008) Landslide hazard assessment using fuzzy logic (Case study: a part of Haraz Watershed). A thesis presented for M.Sc. degree in Watershed Management, Faculty of Natural Resources, Department of Watershed Management, Tarbiat Modarres University, Iran (in Persian).
 Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012a) Application of weightsof evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. doi:10.1007/s1251701205327
 Pourghasemi HR, Pradhan B, Gokceoglu C, Deylami Moezzi K (2012b) A comparative assessment of prediction capabilities of DempsterShafer and weightsofevidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk. doi:10.1080/19475705.2012.662915
 Pourghasemi, HR, Mohammady, M, Pradhan, B (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97: pp. 7184
 Pourghasemi HR, Pradhan B, Gokceoglu C (2012d) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards. doi:10.1007/s1106901202172
 Pourghasemi, HR, Gokceoglu, C, Pradhan, B, Deylami Moezzi, K Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. In: Buchroithner, M, Pradhan, B eds. (2012) Terrigenous mass movements. Springer, Berlin, pp. 2349
 Pourghasemi HR, Goli Jirandeh A, Pradhan B, Xu C, Gokceoglu C (2012) Landslide susceptibility mapping using support vector machine and GIS, J Earth Syst Sci (in press)
 Pourghasemi, HR, Pradhan, B, Gokceoglu, C (2012) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. Appl Mech Mater 225: pp. 486491
 Pradhan, B (2010) Remote sensing and GISbased landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45: pp. 12441256
 Pradhan, B (2010) Application of an advanced fuzzy logic model for landslide susceptibility analysis. Int J Comput Intell Syst 3: pp. 370381
 Pradhan, B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38: pp. 301320
 Pradhan, B (2011) Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis. Environ Ecol Stat 18: pp. 471493
 Pradhan, B (2011) Use of GISbased fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63: pp. 329349
 Pradhan, B (2011) An assessment of the use of an advanced neural network model with five different training strategies for the preparation of landslide susceptibility maps. J Data Sci 9: pp. 6581
 Pradhan B (2012) A comparative study on the predictive ability of the decision tree, support vector machine and neurofuzzy models in landslide susceptibility mapping using GIS. Comput & Geosci, doi:10.1016/j.cageo.2012.08.023
 Pradhan, B, Sezer, EA, Gokceoglu, C, Buchroithner, MF (2010) Landslide susceptibility mapping by neurofuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE Trans Geosci Remote Sens 48: pp. 41644177
 Pradhan, B, 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 Sci Front 14: pp. 143152
 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: pp. 315
 Pradhan, B, Lee, S, Mansor, S, Buchroithner, MF, Jallaluddin, N, 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. Appl Remote Sens 2: pp. 111
 Pradhan, B, Mansor, S, Pirasteh, S, Buchroithner, M (2011) Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int J Remote Sens 32: pp. 40754087
 Pradhan, B, Pirasteh, S (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping. Disaster Adv 3: pp. 2634
 Pradhan, B, Youssef, AM, Varathrajoo, R (2010) Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. GeoSpat Inf Sci 13: pp. 93102
 Rahman Md, R, Saha, SK (2008) Remote sensing, spatial multi criteria evaluation (SMCE) and analytical hierarchy process (AHP) in optimal cropping pattern planning for a flood prone area. J Spatial Sci 53: pp. 2161177
 Remondo, J, Gonzalez, A, Diaz De Teran, JR, Cendrero, A, Fabbri, A, Cheng, CF (2003) Validation of landslide susceptibility maps: examples and applications from a case study in Northern Spain. Nat Hazards 30: pp. 437449
 Saaty, T (1980) The analytical hierarchy Process. McGrawHill, New York
 Sarkar, S, Kanungo, DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogramm Eng Remote Sens 70: pp. 617625
 Sezer, EA, Pradhan, B, Gokceoglu, C (2011) Manifestation of an adaptive neurofuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38: pp. 82088219
 Sharifi, MA, Retsios, V (2004) Site selection for waste disposal through spatial multiple criteria decision analysis. J Telecommun Inf Technol 3: pp. 111
 Sidle RC, Ochiai H (2006) Landslides: process, prediction, and land use. Water Resour Monogr Ser 18:312. doi:10.1029/WM018
 Song, Y, Gong, J, Gao, S, Wang, D, Cui, T, Li, Y, Wei, B (2012) Susceptibility assessment of earthquakeinduced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42: pp. 189199
 Song, KY, Oh, JJ, Choi, J, Park, I, Lee, C, Lee, S (2012) Prediction of landslides using ASTER imagery and data mining models. Adv Space Res 49: pp. 978993
 Suzen, ML, Doyuran, V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45: pp. 665679
 Swets, JA (1988) Measuring the accuracy of diagnostic systems. Science 240: pp. 12851293
 Tagil, S, Jenness, J (2008) GISbased automated landform classification and topographic, land cover and geologic attributes of landforms around the Yazoren Poje, Turkey. J Appl Sci 8: pp. 910921
 Talebi, A, Uijlenhoet, R, Troch, PA (2007) Soil moisture storage and hillslope stability. Nat Hazards Earth Syst Sci 7: pp. 523534
 Tangestani, MH (2009) A comparative study of DemsterShafer and fuzzy models for landslide susceptibility mapping using a GIS: an experience from Zagros Mountains, SW Iran. J Asian Earth Sci 35: pp. 6673
 Terlien, MTJ, Asch, TWJ, Westen, CJ Deterministic modelling in GISbased landslide hazard assessment. In: Carrar, A, Guzzetti, F eds. (1995) Geographical information systems in assessing natural hazards. Kluwer, London, pp. 5777
 Tien Bui, D, Pradhan, B, Lofman, O, Revhaug, I (2012a) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naive Bayes models. Math Probl Eng 2012: pp. 126
 Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2011) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro fuzzy inference system and GIS. Comput Geosci (Article online first available). doi:10.1016/j.cageo.2011.10.031
 Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Landslide susceptibility assessment in the Hoa Binh province of Vietnam using artificial neural network. Geomorphology. doi:10.1016/j.geomorph.2012.04.023, Article online first available
 Tien Bui, D, Pradhan, B, Lofman, O, Revhaug, I, Dick, OB (2012) Spatial prediction of landslide hazards in Vietnam: a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96: pp. 2840
 Vahidnia, MH, Alesheikh, AA, Alimohammadi, A, Hosseinali, F (2010) A GISbased neurofuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36: pp. 11011114
 Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides analysis and control. Special report, vol. 176. Transportation Research Board, National Academy of Sciences, New York, pp. 11–33
 Varnes, DJ (1984) With IAEG Commission on Landslides and Other Mass Movements: landslide hazard zonations: a review of principles and practices. UNESCO, Paris
 Van Westen CJ (2012) Living with landslide risk in Europe: assessment, effects of global change, and risk management strategies, 7th Framework Program Cooperation Theme 6 Environment (including climate change) SubActivity 6.1.3 Natural Hazards, GISbased training package on landslide risk assessment Work Package 7–Dissemination of project results, pp. 133
 Wan S (2012) Entropybased particle swarm optimization with clustering analysis on landslide susceptibility mapping. Environ Earth Sci. doi:10.1007/s1266501218327
 Wang HB, Wu SR, Shi JS, Li B (2011) Qualitative hazard and risk assessment of landslides: a practical framework for a case study in China. Nat Hazards. doi:10.1007/s1106901100081
 Xu C, Dai F, Xu X, Lee YH (2012) GISbased support vector machine modeling of earthquaketriggered landslide susceptibility in the Jianjiang River watershed. China Geomorphol. doi:10.1016/j.geomorph.2011.12.040
 Yalcin A (2005) An investigation on Ardesen (Rize) region on the basis of landslide susceptibility, KTU, PhD Thesis (in Turkish)
 Yalcın, A (2008) GISbased landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72: pp. 112
 Yalcin, A, Reis, S, Aydinoglu, AC, Yomralioglu, T (2011) A GISbased comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85: pp. 274287
 Yao, X, Tham, LG, Dai, FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101: pp. 572582
 Yeon YK, Han JG, Ryu KH (2012) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116:274–283
 Yesilnacar, E, Topal, T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79: pp. 251266
 Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, pp 423.
 Yilmaz, I (2009) A case study from Koyulhisar (SivasTurkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68: pp. 297306
 Yilmaz, I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (TokatTurkey). Comput Geosci 35: pp. 11251138
 Yilmaz, I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61: pp. 821836
 Yilmaz, C, Topal, T, Suzen, ML (2012) GISbased landslide susceptibility mapping using bivariate statistical analysis in Devrek (ZonguldakTurkey). Environ Earth Sci 65: pp. 21612178
 Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2012) Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci. doi:10.1007/s125170120610x
 Title
 GISbased landslide susceptibility mapping with probabilistic likelihood ratio and spatial multicriteria evaluation models (North of Tehran, Iran)
 Journal

Arabian Journal of Geosciences
Volume 7, Issue 5 , pp 18571878
 Cover Date
 20140501
 DOI
 10.1007/s125170120825x
 Print ISSN
 18667511
 Online ISSN
 18667538
 Publisher
 Springer Berlin Heidelberg
 Additional Links
 Topics
 Keywords

 Landslide susceptibility
 Spatial multicriteria evaluation
 Frequency ratio
 GIS
 Tehran metropolitan
 Industry Sectors
 Authors

 H. R. Pourghasemi ^{(1)}
 H. R. Moradi ^{(1)}
 S. M. Fatemi Aghda ^{(2)}
 C. Gokceoglu ^{(3)}
 B. Pradhan ^{(4)}
 Author Affiliations

 1. Department of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modares University (TMU), Noor, Mazandaran, Iran
 2. Department of Engineering Geology, Tarbiat Moallem University, Tehran, Iran
 3. Applied Geology Division, Department of Geological Engineering, Engineering Faculty, Hacettepe University, Ankara, Turkey
 4. Faculty of Engineering, Department of Civil Engineering, University Putra Malaysia, UPM 43400, Serdang, Selangor, Malaysia