Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A Fuzzy-Stochastic Modeling Approach for Multiple Criteria Decision Analysis of Coupled Groundwater-Agricultural Systems

  • 441 Accesses

  • 11 Citations


The complexity of water resources management increases when decisions about environmental and social issues are considered in addition to economic efficiency. Such complexities are further compounded by multiple uncertainties about the consequences of potential management decisions. In this paper, a new fuzzy-stochastic multiple criteria decision-making approach is proposed for water resources management in which a variety of criteria in terms of economic, environmental and social dimensions are identified and taken into account. The goal is to evaluate multiple conflicting criteria under uncertainties and to rank a set of management alternatives. The methodology uses a simulation-optimization water management model of a strongly interacting groundwater-agriculture system to enumerate criteria based on these bio-physical process interactions. Fuzzy and/or qualitative information regarding the decision-making process for which quantitative data is not available are evaluated in linguistic terms. Afterwards, Monte Carlo simulation is applied to combine these information and to generate a probabilistic decision matrix of management alternatives versus criteria in an uncertain environment. Based on this outcome, total performance values of the management alternatives are calculated using ordered weighted averaging. The proposed approach is applied to a real world example, where excessive groundwater withdrawal from the coastal aquifer for irrigated agriculture has resulted in saltwater intrusion, threatening the economical basis of farmers and associated societal impacts. The analysis has provided potential decision alternatives which can serve as a platform for negotiation and further exploration. Furthermore, sensitivity of different inputs to resulting rankings is investigated. It is found that decision makers’ risk aversion and risk taking attitude may yield different rankings. The presented approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. Afshar A, Mariño MA, Saadatpour M, Afshar A (2011) Fuzzy TOPSIS multi-criteria decision analysis applied to Karun reservoirs system. Water Resour Manage 25:545–563

  2. Ahmadisharaf E, Kalyanapu AJ, Chung E (2015) Evaluating the effects of inundation duration and velocity on selection of flood management alternatives using multi-criteria decision making. Water Resour Manage 29:2543–2561

  3. Al-Shoukri SM (2008) Mathematical Modeling of Groundwater Flow in Wadi Ma’awil Catchment, Barka in Sultanate of Oman. Master’s thesis; Arabian Gulf University, Bahrain.

  4. Chou SY, Chang YH, Shen CY (2008) A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. Eur J Oper Res 189(1):132–145

  5. Cullen AC, Frey HC (1999) Probabilistic techniques in exposure assessment: a hand book for dealing with variability and uncertainty in models and inputs. Plenum Press, New York, p 352

  6. Fanghua H, Guanchun (2010) A fuzzy multi-criteria group decision-making model based on weighted Borda scoring method for watershed ecological risk management: a case study of three gorges reservoir area of China. Water Resour Manage 24:2139–2165

  7. FAO (2013). AQUASTAT database, Food and agriculture organization of the United Nations (FAO). On-line database available from: URL http://www.fao.org/nr/water/aquastat/ (last accessed on 13/03/2013).

  8. Grundmann J, Schu¨tze N, Schmitz G, Al-Shaqsi S (2012) Towards an integrated arid zone water management using simulation-based optimisation. Environ Earth Sci 65(5):1381–1394

  9. Grundmann J, Schütze N, Lennartz F (2013) Sustainable management of a coupled groundwater- agriculture hydrosystem using multi-criteria simulation based optimisation. Water Science & Technology 67(3)

  10. Hajkowicz S, Collins K (2007) A review of multiple criteria analysis for water resource planning and management. Water Resour Manag 21(9):1553–1566

  11. Helton JC (1997) Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty. J Stat Comput Simul 57(1-4):3–76

  12. Hyde K, Maier H, Colby C (2004) Reliability-based approach to multicriteria decision analysis for water resources. J Water Resour Plan Manag 130(6):429–438

  13. Jing L, Chen B, Zhang B, Peng H (2013) A hybrid fuzzy stochastic analytical hierarchy process (FSAHP) approach for evaluating ballast water treatment technologies. Environ Systems Res 2(1):1–10

  14. Kalbacher T, Delfs J, Shao H, Wang W, Walther M, Samaniego L, Schneider C, Musolff A, Centler F, Sun F, Hildebrandt A, Liedl R, Borchardt D, Krebs P, Kolditz O ( 2012) The IWAS-ToolBox: software coupling for an integrated water resources management. Environ Earth Sci, 65 (5) (2011), pp. 1367–1380

  15. Kim Y, Chung E, Jun S (2015) Iterative framework for robust reclaimed wastewater allocation in a changing environment using multi-criteria decision making. Water Resour Manage 29:295–311

  16. Li J, Huang GH, Zeng G, Maqsood I, Huang Y (2007) An integrated fuzzy-stochastic modeling approach for risk assessment of groudwater contamination. J Environ Manag 82(2):173–188

  17. Lindhe A, Rosén L, Norberg T, Røstum J, Pettersson TR (2013) Uncertainty modelling in multi-criteria analysis of water safety measures. Environ Systems Decisions 33(2):195–208

  18. Madani K, Lund JR (2011) A Monte-Carlo game theoretic approach for multi-criteria decision making under uncertainty. Adv Water Resour 34(5):607–616

  19. Montanari A, Young G, Savenije HHG, Hughes D, Wagener T, Ren LL, Koutsoyiannis D, Cudennec C, Toth E, Grimaldi S, Blöchl G, Sivapalan M, Beven K, Gupta H, Hipsey M, Schaefli B, Arheimer B, Boegh E, Schymanski SJ, Di Baldassarre G, Yu B, Hubert P, Huang Y, Schumann A, Post DA, Srinivasan V, Harman C, Thompson S, Rogger M, Viglione A, McMillan H, Characklis G, Pang Z, Belyaev V (2013) Panta Rhei- “everything flows”: change in hydrology and society -the IAHS scientific decade 2013–2022. Hydrol Sci J 58(6):1256–1275

  20. Mousavi SM, Jolai F, Tavakkoli-Moghaddam R (2013) A fuzzy stochastic multi-attribute group decision-making approach for selection problems. Group Decis Negot 22(2):207–233

  21. MRMWR (1998) Ministry of regional municipalities and water resources (MRMWR) (1998) water resources master plan for Oman. Sultanate of Oman, Oman

  22. Schütze N, Schmitz G (2010) OCCASION: new planning tool for optimal climate change adaption strategies in irrigation. J Irrig Drain Eng 136(12):836–846

  23. Schütze N, Kloss S, Lennartz F, Al Bakri A, Schmitz G (2012) Optimal planning and operation of irrigation systems under water resource constraints in Oman considering climatic uncertainty. J Environ Earth Sci 65(5):1511–1521

  24. Seegert J, Markova D, Kolditz O, Krebs P, Borchardt D (2014) Integrated water resources management under different hydrological, climatic and socio-economic conditions: results and lessons learned from a transdisciplinary IWRM project IWAS. Environ Earth Sci 72:46677–4687

  25. Simonovic SP, Nirupama (2005) A spatial multi-objective decision-making under uncertainty for water resources management. J Hydroinf 7:117–133

  26. Stewart T (2005) Dealing with uncertainties in MCDA multiple criteria decision analysis: state of the art surveys. Springer, New York, pp 445–466

  27. Subagadis YH (2015) A new integrated modeling approach to support management decisions of water resources systems under multiple uncertainties. Ph.D thesis, TU Dresden: URL http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-189212

  28. Subagadis YH, Grundmann J, Schütze N, Schmitz G (2014a) An integrated approach to conceptualise hydrological and socio-economic interaction for supporting management decisions of coupled groundwater-agricultural systems. Environ Earth Sci 72:4917–4933. doi:10.1007/s12665-014-3238-1

  29. Subagadis YH, Schütze N, Grundmann J (2014b) Multi-criteria multi-stakeholder decision analysis using fuzzy-stochastic approach for hydrosystem management. Proc IAHS 364:464–469. doi:10.5194/piahs-364-464-2014

  30. Tesfamariam S, Sadiq R (2008) Probabilistic risk analysis using ordered weighted averaging (OWA) operators. Stoch Env Res Risk A 22(1):1–15

  31. Vrugt JA, Robinson BA, Hyman JM (2009) Self-adaptive multimethod search for global optimization in real-parameter spaces. Evolutionary Computation, IEEE Trans 13(2):243–259

  32. Wong ETT, Norman G, Flanagan R (2000) A fuzzy stochastic technique for project selection. Construction Management Economics 18(4):407–414

  33. Xu YP, Tung YK (2008) Decision-making in water management under uncertainty. Water Resour Manag 22(5):535–550

  34. Xu Y, Tung Y (2009) Decision rules for water resources management under uncertainty. J Water Resour Plan Manag 135(3):149–159

  35. Yager RR (1988) On ordered weighted averaging aggregation operators in multi criteria decision making. IEEE Trans Syst Man cybern 18:183–190

  36. Yager RR (1996) Quantifier guided aggregation using OWA operators. Int J Intell Syst 11:49–73

  37. Zadeh L (1965) Fuzzy sets. Information Controls 8:338–353

  38. Zagonari F, Rossi C (2013) A heterogeneous multi-criteria multi-expert decision-support system for scoring combinations of flood mitigation and recovery options. Environ Model Softw 49:152–165

  39. Zarghami M, Szidarovszky F, Ardakanian R (2008) A fuzzy-stochastic OWA model for robust multi-criteria decision making. Fuzzy Optim Decis Making 7(1):1–15

  40. Zimmermann H (1991) Fuzzy set theory and its application, 2nd edn. Springer, Berlin

Download references


This paper is based on the Ph.D dissertation of the first author “A new integrated modeling approach to support management decisions of water resources systems under multiple uncertainties.” The first author would like to gratefully acknowledge the funding received towards his Ph.D from Graduate Academy of TU Dresden.

Author information

Correspondence to Yohannes Hagos Subagadis.

Ethics declarations

Conflict of Interest

No conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Subagadis, Y.H., Schütze, N. & Grundmann, J. A Fuzzy-Stochastic Modeling Approach for Multiple Criteria Decision Analysis of Coupled Groundwater-Agricultural Systems. Water Resour Manage 30, 2075–2095 (2016). https://doi.org/10.1007/s11269-016-1270-5

Download citation


  • Fuzzy-stochastic approach
  • Simulation-optimization
  • Decision-making under uncertainty
  • Groundwater
  • Agriculture