Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran

  • Mahmoud RezaeiEmail author
  • Farshad Amiraslani
  • Najmeh Neysani Samani
  • Kazem Alavipanah
Original Paper


Flooding is one of the most problematic natural events affecting urban areas. In this regard, developing flooding models plays a crucial role in reducing flood-induced losses and assists city managers to determine flooding-prone areas (FPAs). The aim of this study is to investigate on the prediction capability of fuzzy analytical hierarchy process (FAHP) and Mamdani fuzzy inference system (MFIS) methods as two completely and semi-knowledge-based models to identify FPAs in Tehran, Iran. Six flooding conditioning factors including density of channel, distance from channel, land use, elevation, slope, and water discharge were extracted from various geo-spatial datasets. A total of 62 flooding locations were identified in the study area based on the existing reports and field surveys. Of these, 44 (70%) floods were randomly selected as training data and the remaining 18 (30%) cases were used for the validation purposes. After the data preparation step, data were processed by means of two statistical (FAHP) and soft computing (MFIS) methods. Unlike most statistical and soft computing approaches which use flooding inventory data for both training and evaluation of models, only conditioning factor was involved in data processing and inventory data were used in the current study to assess models prediction accuracy. Also, the efficiency of two approaches was evaluated by pixel matching (PM) and area under curve to validate the prediction capability of models. The prediction rate for MFIS and FAHP was 89% and 84%, respectively. Moreover, according to the results obtained from PM, it was found out that about 90% of known flooding locations fell in high-risk areas, whereas it was 83% for FAHP, indicating that flooding susceptibility map of MFIS has higher performance.


Flooding Modeling Fuzzy analytic hierarchical processes Mamdani fuzzy inference system Aggregation methods 



  1. Abebe Y, Kabir G, Tesfamariam S (2018) Assessing urban areas vulnerability to pluvial flooding using GIS applications and Bayesian Belief Network model. J Clean Prod 174:1629–1641. CrossRefGoogle Scholar
  2. Akgun A, Türk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environ Earth Sci 61(3):595–611. CrossRefGoogle Scholar
  3. Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6):1127–1143. CrossRefGoogle Scholar
  4. Al-Hanbali A, Alsaaideh B, Kondoh A (2011) Using GIS-based weighted linear combination analysis and remote sensing techniques to select optimum solid waste disposal sites within Mafraq City, Jordan. J Geogr Inf Syst 03(04):267–278. CrossRefGoogle Scholar
  5. Asklany SA, Elhelow K, Youssef IK, Abd El-wahab M (2011) Rainfall events prediction using rule-based fuzzy inference system. Atmos Res 101(1–2):228–236. CrossRefGoogle Scholar
  6. Aydin A, Eker R (2016) Fuzzy rule-based landslide susceptibility mapping in Yığılca Forest District (Northwest of Turkey). J Fac For Istanb Univ 66(2):559–571. CrossRefGoogle Scholar
  7. Bai Y, Zhuang H, Wang D (2006) Advanced fuzzy logic technologies in industrial applications. Springer, BerlinCrossRefGoogle Scholar
  8. Balezentiene L, Streimikiene D, Balezentis T (2013) Fuzzy decision support methodology for sustainable energy crop selection. Renew Sustain Energy Rev 17:83–93. CrossRefGoogle Scholar
  9. Barati-Harooni A, Najafi-Marghmaleki A, Hoseinpour S-A, Tatar A, Karkevandi-Talkhooncheh A, Hemmati-Sarapardeh A, Mohammadi AH (2019) Estimation of minimum miscibility pressure (MMP) in enhanced oil recovery (EOR) process by N2 flooding using different computational schemes. Fuel 235:1455–1474. CrossRefGoogle Scholar
  10. Bathrellos GD, Karymbalis E, Skilodimou HD, Gaki-Papanastassiou K, Baltas EA (2016) Urban flood hazard assessment in the basin of Athens Metropolitan city, Greece. Environ Earth Sci 75(4):319. CrossRefGoogle Scholar
  11. Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans Geosci Remote Sens 28(4):540–552. CrossRefGoogle Scholar
  12. Boroushaki S, Malczewski J (2008) Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Comput Geosci 34(4):399–410. CrossRefGoogle Scholar
  13. Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5(6):853–862. CrossRefGoogle Scholar
  14. Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9):948. CrossRefGoogle Scholar
  15. Castillo O, Melin P (2007) Type-2 fuzzy logic. In: Castillo O, Melin P (eds) Type-2 fuzzy logic: theory and applications. Springer, Berlin, pp 29–43. CrossRefGoogle Scholar
  16. Chang L-C, Chang F-J, Tsai Y-H (2005) Fuzzy exemplar-based inference system for flood forecasting. Water Resour Res. CrossRefGoogle Scholar
  17. Chau KW, Wu CL, Li YS (2005) Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng 10(6):485–491. CrossRefGoogle Scholar
  18. Chen VYC, Lien H-P, Liu C-H, Liou JJH, Tzeng G-H, Yang L-S (2011) Fuzzy MCDM approach for selecting the best environment-watershed plan. Appl Soft Comput 11(1):265–275. CrossRefGoogle Scholar
  19. Chen W, Panahi M, Pourghasemi HR (2017a) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310–324. CrossRefGoogle Scholar
  20. Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017b) Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327. CrossRefGoogle Scholar
  21. Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Ahmad B Bin (2019) Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. CATENA 172:212–231. CrossRefGoogle Scholar
  22. Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2019) An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 651:2087–2096. CrossRefGoogle Scholar
  23. Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375(3–4):613–626. CrossRefGoogle Scholar
  24. Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gómez-Gutiérrez Á, Rotigliano E, Agnesi V (2015) 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 242:49–64. CrossRefGoogle Scholar
  25. Czabanski R, Jezewski M, Leski J (2017) Introduction to fuzzy systems. Springer, Cham, pp 23–43. CrossRefGoogle Scholar
  26. Darabi H, Choubin B, Rahmati O, Torabi Haghighi A, Pradhan B, Kløve B (2019) Urban flood risk mapping using the GARP and QUEST models: a comparative study of machine learning techniques. J Hydrol 569:142–154. CrossRefGoogle Scholar
  27. Eastman J (2012) IDRISI selva tutorial. Idrisi Production, Clark Labs-Clark University, pp 51–63Google Scholar
  28. Ergu D, Kou G, Shi Y, Shi Y (2014) Analytic network process in risk assessment and decision analysis. Comput Oper Res 42:58–74. CrossRefGoogle Scholar
  29. Falah F, Rahmati O, Rostami M, Ahmadisharaf E, Daliakopoulos IN, Pourghasemi HR (2019) Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. In: Pourghasemi HR, Gokceoglu C (eds) Spatial modeling in GIS and R for earth and environmental sciences. Elsevier, Amsterdam, pp 323–336. CrossRefGoogle Scholar
  30. Feizizadeh B, Shadman Roodposhti M, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221. CrossRefGoogle Scholar
  31. Felicísimo ÁM, 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(2):175–189. CrossRefGoogle Scholar
  32. Ganji-Azad E, Rafiee-Taghanaki S, Rezaei H, Arabloo M, Zamani HA (2014) Reservoir fluid PVT properties modeling using adaptive neuro-fuzzy inference systems. J Nat Gas Sci Eng 21:951–961. CrossRefGoogle Scholar
  33. Guzha AC, Rufino MC, Okoth S, Jacobs S, Nóbrega RLB (2018) Impacts of land use and land cover change on surface runoff, discharge and low flows: evidence from East Africa. J Hydrol Reg Stud 15:49–67. CrossRefGoogle Scholar
  34. Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126(3):39. CrossRefGoogle Scholar
  35. Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu A-X, Kazakis N (2018a) Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ 621:1124–1141. CrossRefGoogle Scholar
  36. Hong H, Tsangaratos P, Ilia I, Liu J, Zhu A-X, Chen W (2018b) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci Total Environ 625:575–588. CrossRefGoogle Scholar
  37. Hussu A (1995) Fuzzy control and defuzzification. Mechatronics 5(5):513–526. CrossRefGoogle Scholar
  38. Ishizaka A (2014) Comparison of fuzzy logic, AHP, FAHP and hybrid fuzzy AHP for new supplier selection and its performance analysis. Int J Integr Supply Manag 9(1/2):1. CrossRefGoogle Scholar
  39. Janeela Theresa MM, Joseph Raj V (2013) Fuzzy based genetic neural networks for the classification of murder cases using Trapezoidal and Lagrange interpolation membership functions. Appl Soft Comput 13(1):743–754. CrossRefGoogle Scholar
  40. Jha AK, Bloch R, Lamond J (2012) Cities and flooding. The World Bank, Washington. CrossRefGoogle Scholar
  41. Johnson LM, Rezaee R, Kadkhodaie A, Smith G, Yu H (2018) Geochemical property modelling of a potential shale reservoir in the Canning Basin (Western Australia), using Artificial Neural Networks and geostatistical tools. Comput Geosci 120:73–81. CrossRefGoogle Scholar
  42. Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hübl J (2019) Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. Sci Total Environ 653:801–814. CrossRefGoogle Scholar
  43. Karkevandi-Talkhooncheh A, Sharifi M, Ahmadi M (2018) Application of hybrid adaptive neuro-fuzzy inference system in well placement optimization. J Pet Sci Eng 166:924–947. CrossRefGoogle Scholar
  44. Kavzoglu T, Kutlug Sahin E, Colkesen I (2015) Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng Geol 192:101–112. CrossRefGoogle Scholar
  45. Keshwani DR, Jones DD, Meyer GE, Brand RM (2008) Rule-based Mamdani-type fuzzy modeling of skin permeability. Appl Soft Comput 8(1):285–294. CrossRefGoogle Scholar
  46. Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016) A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards 83(2):947–987. CrossRefGoogle Scholar
  47. Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Tien Bui D (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627:744–755. CrossRefGoogle Scholar
  48. Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67(1):251–264. CrossRefGoogle Scholar
  49. Kim TH, Kim B, Han K-Y (2019) Application of fuzzy TOPSIS to flood hazard mapping for levee failure. Water 11(3):592. CrossRefGoogle Scholar
  50. Kordi M (2008) Comparison of fuzzy and crisp analytic hierarchy process (AHP) methods for spatial multicriteria decision analysis in GIS. Retrieved from
  51. Le Van S, Chon BH (2017) Evaluating the critical performances of a CO2: enhanced oil recovery process using artificial neural network models. J Pet Sci Eng 157:207–222. CrossRefGoogle Scholar
  52. Lee MJ, Kang J, Jeon S (2012) Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In: 2012 IEEE international geoscience and remote sensing symposium. IEEE, pp 895–898.
  53. Lee M-J, 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 74(1):413–429. CrossRefGoogle Scholar
  54. Lin F, Ying H, MacArthur RD, Cohn JA, Barth-Jones D, Crane LR (2007) Decision making in fuzzy discrete event systems. Inf Sci 177(18):3749–3763. CrossRefGoogle Scholar
  55. Liu K, Li Z, Yao C, Chen J, Zhang K, Saifullah M (2016) Coupling the k-nearest neighbor procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting. Int J Sedim Res 31(2):149–158. CrossRefGoogle Scholar
  56. Lombardo L, Bachofer F, Cama M, Märker M, Rotigliano E (2016) Exploiting maximum entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north-eastern Sicily, Italy). Earth Surf Proc Land 41(12):1776–1789. CrossRefGoogle Scholar
  57. Mahmoud SH, Gan TY (2018) Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. J Clean Prod 196:216–229. CrossRefGoogle Scholar
  58. Manfreda S, Samela C (2019) A digital elevation model based method for a rapid estimation of flood inundation depth. J Flood Risk Manag. CrossRefGoogle Scholar
  59. Manfreda S, Di Leo M, Sole A (2011) Detection of flood-prone areas using digital elevation models. J Hydrol Eng 16(10):781–790. CrossRefGoogle Scholar
  60. Manfreda S, Nardi F, Samela C, Grimaldi S, Taramasso AC, Roth G, Sole A (2014) Investigation on the use of geomorphic approaches for the delineation of flood prone areas. J Hydrol 517:863–876. CrossRefGoogle Scholar
  61. Manfreda S, Samela C, Gioia A, Consoli GG, Iacobellis V, Giuzio L, Sole A (2015) Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models. Nat Hazards 79(2):735–754. CrossRefGoogle Scholar
  62. Mazloumzadeh SM, Shamsi M, Nezamabadi-pour H (2008) Evaluation of general-purpose lifters for the date harvest industry based on a fuzzy inference system. Comput Electron Agric 60(1):60–66. CrossRefGoogle Scholar
  63. Mendel JM (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345–377. CrossRefGoogle Scholar
  64. Mukerji A, Chatterjee C, Raghuwanshi NS (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. J Hydrol Eng 14(6):647–652. CrossRefGoogle Scholar
  65. Naghibi SA, Pourghasemi HR, Dixon B (2016) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188(1):44. CrossRefGoogle Scholar
  66. Nicu IC, Asăndulesei A (2018) GIS-based evaluation of diagnostic areas in landslide susceptibility analysis of Bahluieț River Basin (Moldavian Plateau, NE Romania). Are neolithic sites in danger? Geomorphology 314:27–41. CrossRefGoogle Scholar
  67. Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2014) Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards 71(1):523–547. CrossRefGoogle Scholar
  68. Oh H-J, 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. CrossRefGoogle Scholar
  69. Öztaysi B, Behret H, Kabak Ö, Sarı IU, Kahraman C (2013) Fuzzy inference systems for disaster response. Atlantis Press, Paris, pp 75–94. CrossRefGoogle Scholar
  70. Pappenberger F, Frodsham K, Beven K, Romanowicz R, Matgen P (2007) Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations. Hydrol Earth Syst Sci 11(2):739–752. CrossRefGoogle Scholar
  71. Park B, Chen YR, Whittaker AD, Miller RK, Hale DS (1994) Neural network modeling for beef sensory evaluation. Trans ASAE 37(5):1547–1553. CrossRefGoogle Scholar
  72. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996. CrossRefGoogle Scholar
  73. Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD (2013) A comparative assessment of prediction capabilities of Dempster–Shafer and Weights-of-evidence models in landslide susceptibility mapping using GIS. Geomat Nat Hazards Risk 4(2):93–118. CrossRefGoogle Scholar
  74. Radmehr A, Araghinejad S (2015) Flood vulnerability analysis by fuzzy spatial multi criteria decision making. Water Resour Manag 29(12):4427–4445. CrossRefGoogle Scholar
  75. Rahmati O, Pourghasemi HR (2017) Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models. Water Resour Manag 31(5):1473–1487. CrossRefGoogle Scholar
  76. Rahmati O, Pourghasemi HR, Zeinivand H (2016a) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int 31(1):42–70. CrossRefGoogle Scholar
  77. Rahmati O, Zeinivand H, Besharat M (2016b) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Hazards Risk 7(3):1000–1017. CrossRefGoogle Scholar
  78. Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O (2015) Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci Inf 8(4):867–883. CrossRefGoogle Scholar
  79. Razavi Termeh SV, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438–451. CrossRefGoogle Scholar
  80. Roodposhti MS, Rahimi S, Beglou MJ (2014) PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat Hazards 73(1):77–95. CrossRefGoogle Scholar
  81. Rostamzadeh R, Ghorabaee MK, Govindan K, Esmaeili A, Nobar HBK (2018) Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach. J Clean Prod 175:651–669. CrossRefGoogle Scholar
  82. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York, pp 579–606. CrossRefGoogle Scholar
  83. Sadollah A (2018) Introductory chapter: which membership function is appropriate in fuzzy system? In: Sadollah A (ed) Fuzzy logic based in optimization methods and control systems and its applications. InTech, London. CrossRefGoogle Scholar
  84. Samela C, Manfreda S, De Paola F, Giugni M, Sole A, Fiorentino M (2016) DEM-based approaches for the delineation of flood-prone areas in an ungauged basin in Africa. J Hydrol Eng 21(2):06015010. CrossRefGoogle Scholar
  85. Samela C, Troy TJ, Manfreda S (2017) Geomorphic classifiers for flood-prone areas delineation for data-scarce environments. Adv Water Resour 102:13–28. CrossRefGoogle Scholar
  86. Seckin N, Cobaner M, Yurtal R, Haktanir T (2013) Comparison of artificial neural network methods with L-moments for estimating flood flow at ungauged sites: the case of East Mediterranean River Basin, Turkey. Water Resour Manag 27(7):2103–2124. CrossRefGoogle Scholar
  87. 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. CrossRefGoogle Scholar
  88. Shafizadeh-Moghadam H, Valavi R, Shahabi H, Chapi K, Shirzadi A (2018) Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. J Environ Manag 217:1–11. CrossRefGoogle Scholar
  89. Sharma CS, Behera MD, Mishra A, Panda SN (2011) Assessing flood induced land-cover changes using remote sensing and fuzzy approach in Eastern Gujarat (India). Water Resour Manag 25(13):3219–3246. CrossRefGoogle Scholar
  90. Sicat RS, Carranza EJM, Nidumolu UB (2005) Fuzzy modeling of farmers’ knowledge for land suitability classification. Agric Syst 83(1):49–75. CrossRefGoogle Scholar
  91. Simonton DK (1977) Cross-sectional time-series experiments: some suggested statistical analyses. Psychol Bull 84(3):489–502. CrossRefGoogle Scholar
  92. Stefanidis S, Stathis D (2013) Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards 68(2):569–585. CrossRefGoogle Scholar
  93. Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79. CrossRefGoogle Scholar
  94. Tehrany MS, Lee M-J, Pradhan B, Jebur MN, Lee S (2014a) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72(10):4001–4015. CrossRefGoogle Scholar
  95. Tehrany MS, Pradhan B, Jebur MN (2014b) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343. CrossRefGoogle Scholar
  96. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211. CrossRefGoogle Scholar
  97. Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239(1–4):132–147. CrossRefGoogle Scholar
  98. Turksen IB (1991) Measurement of membership functions and their acquisition. Fuzzy Sets Syst 40(1):5–38. CrossRefGoogle Scholar
  99. Vahidnia MH, Alesheikh AA, Alimohammadi A (2009) Hospital site selection using fuzzy AHP and its derivatives. J Environ Manag 90(10):3048–3056. CrossRefGoogle Scholar
  100. Wang Y, Li Z, Tang Z, Zeng G (2011) A GIS-based spatial multi-criteria approach for flood risk assessment in the Dongting Lake Region, Hunan, Central China. Water Resour Manag 25(13):3465–3484. CrossRefGoogle Scholar
  101. Wu D (2012) Twelve considerations in choosing between Gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers. In: 2012 IEEE international conference on fuzzy systems. IEEE, pp 1–8.
  102. Youssef AM, Pradhan B, Hassan AM (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ Earth Sci 62(3):611–623. CrossRefGoogle Scholar
  103. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. CrossRefGoogle Scholar
  104. Zhang J, Su Y, Wu J, Liang H (2015) GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China. Comput Electron Agric 114:202–211. CrossRefGoogle Scholar
  105. Zhao G, Xu Z, Pang B, Tu T, Xu L, Du L (2019) An enhanced inundation method for urban flood hazard mapping at the large catchment scale. J Hydrol 571:873–882. CrossRefGoogle Scholar
  106. Zhu A-X, Wang R, Qiao J, Qin C-Z, Chen Y, Liu J, Zhu T (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138. CrossRefGoogle Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  • Mahmoud Rezaei
    • 1
    Email author
  • Farshad Amiraslani
    • 1
    • 2
  • Najmeh Neysani Samani
    • 1
  • Kazem Alavipanah
    • 1
  1. 1.Department of Remote Sensing and GIS, Faculty of GeographyUniversity of TehranTehranIran
  2. 2.NUIST-Reading InstituteNanjing University of Information, Science and TechnologyNanjingChina

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