Environmental Earth Sciences

, 78:625 | Cite as

Comparison analytic network and analytical hierarchical process approaches with feature selection algorithm to predict groundwater quality

  • Marzieh Mokarram
  • Hamid Reza PourghasemiEmail author
  • John P. Tiefenbacher
Original Article


Groundwater quality assessment is important for potability and for industrial and agricultural uses of water. Groundwater quality is a measure of contamination by chemicals, biological organisms, sediments, and heat. Reductions of groundwater quality in some areas due to high rates of consumption have prompted the identification of regions in which extraction will be focused. In this study, geographic information systems (GIS)-based analytical network process (ANP) and analytical hierarchical process (AHP) multiple-criteria decision-making techniques using a fuzzy-quantifier algorithm were devised to model groundwater quality in northern Fars Province, Iran. Groundwater quality was assessed by measuring calcium (Ca), chlorine (Cl), magnesium (Mg), thorium (Th), sodium (Na), sulfate (SO4), electrical conductivity (EC), and total dissolved solids (TDS). A membership function based on World Health Organization (WHO) groundwater quality standards was used to create a fuzzy map of each parameter in ArcGIS. Fuzzy maps were generated for each layer using a trapezoidal membership function. The AHP and ANP methods provided weights for each layer to generate groundwater quality maps. Using a feature-selection algorithm, the relative importance of the factors that affect groundwater quality was determined. The results show that, according to the AUC values, ANP generates higher accuracy than fuzzy-AHP (0.954 in comparison to 0.845). The feature-selection algorithm indicates that Ca, Cl, EC, and Na had the greatest impact on groundwater quality conditions. Using ANP and selecting the most important factors can be an economical way, in terms of time and money, to produce highly accurate information that can be used to predict local groundwater quality.


Groundwater quality ANP AHP Feature selection Fuzzy method Geographic information system (GIS) 



  1. Asakereh A, Soleymani M, Sheikhdavoodi MJ (2017) A GIS-based Fuzzy-AHP method for the evaluation of solar farms locations: case study in Khuzestan province, Iran. Solar Energy 155:342–353. CrossRefGoogle Scholar
  2. Azimi S, Moghaddam MA, Monfared SAH (2018) Spatial assessment of the potential of groundwater quality using fuzzy AHP in GIS. Arab J Geosci 11(7):142CrossRefGoogle Scholar
  3. Chen D, Dong W, Shah HC (1988) Earthquake recurrence relationships from fuzzy earthquake magnitudes. Soil Dyn Earthq Eng 7:136–142. CrossRefGoogle Scholar
  4. Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151:155–176. CrossRefGoogle Scholar
  5. Do TA, Lawrence AM, Tia M, Bergin MJ (2013) Importance of insulation at the bottom of mass concrete placed on soil with high groundwater. Transport Res Rec 2342(1):113–120CrossRefGoogle Scholar
  6. Garuti C, Spencer I (2007) Parallels between the analytic hierarchy and network processes (AHP/ANP) and fractal geometry. Math Comput Model 46:926–934. CrossRefGoogle Scholar
  7. Görener A (2012) Comparing AHP and ANP: an application of strategic decisions making in a manufacturing company. Int J Bus Soc Sci 3(11):194–208Google Scholar
  8. Huang H-P, Yang K-C, Lin B-W (2013) Statistical evaluation of the effect of earthquake with other related factors on landslide susceptibility: using the watershed area of Shihmen reservoir in Taiwan as a case study. Environ Earth Sci 69:2151–2166. CrossRefGoogle Scholar
  9. Keshavarzi D, Soltani Z, Ebrahimi M, Soltani A, Nutifafa GG, Soltani F, Faramarzi H, Amraee K, Hassanzadeh A (2017) Monthly prevalence and diversity of mosquitoes (Diptera: Culicidae) in Fars Province, Southern Iran. Asian Pac J Trop Dis 7:112–120. CrossRefGoogle Scholar
  10. Lee H, Lee S, Park Y (2009) Selection of technology acquisition mode using the analytic network process. Math Comput Model 49:1274–1282. CrossRefGoogle Scholar
  11. Levy JK, Taji K (2007) Group decision support for hazards planning and emergency management: a group analytic network process (GANP) approach. Math Comput Model 46:906–917. CrossRefGoogle Scholar
  12. Liang X, Sun X, Shu G, Sun K, Wang X, Wang X (2013) Using the analytic network process (ANP) to determine method of waste energy recovery from engine. Energy Convers. Manag 66:304–311. CrossRefGoogle Scholar
  13. Liu S, Tai H, Ding Q, Xu L, Wei Y (2013) A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model 58:458–465. CrossRefGoogle Scholar
  14. Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New YorkGoogle Scholar
  15. McBratney AB, Odeh IOA (1997) Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77:85–113. CrossRefGoogle Scholar
  16. Merrouni AA, Elwali Elalaoui F, Ghennioui A, Mezrhab A, Mezrhab A (2018) A GIS-AHP combination for the sites assessment of large-scale CSP plants with dry and wet cooling systems. Case study: Eastern Morocco. Solar Energy 166:2–12. CrossRefGoogle Scholar
  17. Mertens KC, de Baets B, Verbeke LPC, de Wulf RR (2006) A sub-pixel mapping algorithm based on sub-pixel/pixel spatial attraction models. Int J Remote Sens 27:3293–3310. CrossRefGoogle Scholar
  18. Mokarram M, Sathyamoorthy D (2016) Investigation of the relationship between drinking water quality based on content of inorganic components and landform classes using fuzzy AHP (case study: south of Firozabad, west of Fars province, Iran). Drink Water Eng Sci 9:57–67. CrossRefGoogle Scholar
  19. Naseriparsa M, Bidgoli AM, Varaee T (2014) A hybrid feature selection method to improve performance of a group of classification algorithms. Int J Comput Appl 1403–2372Google Scholar
  20. Nasr As, Rezaei M, Dashti Barmaki M (2013) Groundwater contamination analysis using fuzzy water quality index (FWQI): Yazd province, Iran. JGeope 3:47–55Google Scholar
  21. Nieto-Morote A, Ruz-Vila F (2011) A fuzzy AHP multi-criteria decision-making approach applied to combined cooling, heating and power production systems. Int J Inf Technol Decis Mak 10:497–517. CrossRefGoogle Scholar
  22. Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Syst 4:313–332. CrossRefGoogle Scholar
  23. Roy R, Majumder M, Barman RN (2017) Assessment of water quality by RSM and ANP: a case study in Tripura, India. Asian J Water Environ Pollut 14:51–58. CrossRefGoogle Scholar
  24. Saaty TL (1980) The analytic hierarchy process : planning, priority setting, resource allocation. McGraw-Hill International Book Co, New YorkGoogle Scholar
  25. Saaty TL (1996) Decision making with dependence and feedback: the analytic network process 4922. RWS PublGoogle Scholar
  26. Saaty TL (2005) Theory and applications of the analytic network process: decision making with benefits, opportunities, costs, and risks. RWS Publications, BristolGoogle Scholar
  27. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1:83–98Google Scholar
  28. Saaty TL, Vargas LG (1998) Diagnosis with dependent symptoms: Bayes theorem and the analytic hierarchy process. Oper Res 46:491–502. CrossRefGoogle Scholar
  29. Sagir M, Ozturk ZK (2010) Exam scheduling: mathematical modeling and parameter estimation with the analytic network process approach. Math Comput Model 52:930–941. CrossRefGoogle Scholar
  30. Şener E, Şener Ş, Davraz A (2018) Groundwater potential mapping by combining fuzzy-analytic hierarchy process and GIS in Beyşehir Lake Basin, Turkey. Arab J Geosci 11:1–21CrossRefGoogle Scholar
  31. Shobha G, Gubbi J, Raghavan KS, Kaushik LK, Palaniswami M (2013) A novel fuzzy rule based system for assessment of ground water potability: a case study in South India. Magnesium (Mg) 30:10Google Scholar
  32. Soroush F, Mousavi SF, Gharechahi A (2011) A Fuzzy industrial water quality index: case study of Zayandehrud River System. Trans Civil Environ Eng 35:131–136Google Scholar
  33. Tavanpour N, Ghaemi AA (2016) Zoning of Fars Province in terms of rain-fed winter wheat cultivation based on precipitation and morphological factors. Trends Life Sci 5:544–555Google Scholar
  34. USEPA (1993) All the water in the world: magnificient ground water connection. United States Environmental Protection Agency, WashingtonGoogle Scholar
  35. Wang J-J, Jing Y-Y, Zhang C-F, Shi G-H, Zhang X-T (2008) A fuzzy multi-criteria decision-making model for trigeneration system. Energy Policy 36:3823–3832. CrossRefGoogle Scholar
  36. Wiguna KA, Sarno R, Ariyani NF (2016) Optimization solar farm site selection using multi-criteria decision making fuzzy AHP and PROMETHEE: case study in Bali. In: 2016 International Conference on Information and Communication Technology and Systems (ICTS). IEEE, pp 237–243.
  37. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Range and Watershed Management, College of Agriculture and Natural Resources of DarabShiraz UniversityShirazIran
  2. 2.Department of Natural Resources and Environmental Engineering, College of AgricultureShiraz UniversityShirazIran
  3. 3.Department of GeographyTexas State UniversitySan MarcosUSA

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