Abstract
Water Distribution System (WDS) are strategic infrastructures in all countries. In recent decades, several optimization-based frameworks have been developed to detect potential contaminant events through risk mitigation strategies using the optimal placement of water quality sensors. However, outcomes of the optimization models may not sufficiently represent the priorities of involved stakeholders in real-world case studies where conflicts of interest arise. Therefore, conflict resolution frameworks that consider stakeholder engagement are needed to enhance the security of a WDS through an agreed-upon layout of an optimal number of sensors. In this study, according to the uncertain nature of input contamination into the network, a Fuzzy Transformation Method (FTM) and a Monte-Carlo Simulation (MCS) were employed to address uncertainties in the EPANET simulation model. A fuzzy-based NSGA-II optimization model was developed to determine trade-offs among targets of stakeholders. Social Choice Theories (SCTs) was used to specify the compromise solutions on each trade-off curve. Using the possibility degree method, the obtained fuzzy intervals were ranked based on each stakeholder’s point of view. Finally, the most appropriate SCTs were introduced through a negotiation method (unanimity fallback bargaining) at each confidence level. The application of the proposed methodology was evaluated in the WDS of the city of Lamerd in Fars, Iran. The capability of the proposed methodology in selecting socio-optimal sensor placement was compared with the results of previous studies. The obtained results demonstrated that the proposed framework yielded a reliable outcome and enhance the decision-making condition for stakeholders to improve the security of a WDS.
Similar content being viewed by others
Notes
Non-dominated Sorting Genetic Algorithm II (NSGA-II).
Progressive Genetic Algorithm (PGA).
Non-dominated Archiving Ant Colony Optimization (NA-ACO).
References
Alizadeh MR, Nikoo MR, Rakhshandehroo GR (2017a) Developing a multi-objective conflict-resolution model for optimal groundwater management based on fallback bargaining models and social choice rules: a case study. Water Resour Manage 31(5):1457–1472. https://doi.org/10.1007/s11269-017-1588-7
Alizadeh MR, Nikoo MR, Rakhshandehroo GR (2017b) Hydro-environmental management of groundwater resources: a fuzzy-based multi-objective compromise approach. J Hydrol 551:540–554. https://doi.org/10.1016/j.jhydrol.2017.06.011
Arad J, Housh M, Perelman L, Ostfeld A (2013) A dynamic thresholds scheme for contaminant event detection in water distribution systems. Water Res 47(5):1899–1908. https://doi.org/10.1016/j.watres.2013.01.017
Aral MM, Guan J, Maslia ML (2008) A multi-objective optimization algorithm for sensor placement in water distribution systems. In: World environmental and water resources congress 2008, pp 1–11 https://doi.org/10.1061/40976(316)510
Aral MM, Guan J, Maslia ML (2009) Optimal design of sensor placement in water distribution networks. Journal of Water Resources Planning and Management 136(1):5–18. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000001
Austin RG, Choi CY, Preis A, Ostfeld A, Lansey K (2009) Multi-objective sensor placements with improved water quality models in a network with multiple junctions. In Proc. world environmental and water resources congress. ASCE, Reston, USA 2009:451–459. https://doi.org/10.1061/41036(342)44
Bazargan-Lari MR (2014) An evidential reasoning a roach to optimal monitoring of drinking water distribution systems for detecting deliberate contamination events. Journal of Cleaner Production 78:1–14. https://doi.org/10.1016/j.jclepro.2014.04.061
Berry J, Boman E, Riesen LA, Hart WE, Phillips CA, Watson JP (2012) User’s manual: TEVA-SPOT toolkit 2.5.2. The United States Environmental Protection Agency, Cincinnati
Berry J, Boman E, Riesen LA, Hart WE, Phillips CA, Watson JP (2008) User’s manual: TEVA-SPOT toolkit 2.2. Sandia National Laboratories, Albuquerque
Brams SJ, Kilgour DM (2001) Fallback bargaining. Group Decis Negot 10(4):287–316. https://doi.org/10.1023/A:1011252808608
Daneshmand F, Karimi A, Nikoo MR, Bazargan-Lari MR, Adamowski J (2014) Mitigating socio-economic-environmental impacts during drought periods by optimizing the conjunctive management of water resources. Water Res Manag 28(6):1517–1529. https://doi.org/10.1007/s11269-014-0549-7
Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International conference on parallel problem solving from nature, Springer Berlin Heidelberg, pp 849–858. https://doi.org/10.1007/3-540-45356-3_83
Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017
Diwold K, Tk R, Middendorf M (2010) Sensor placement in water networks using a population-based ant colony optimization algorithm. Comput Collective Intell Technol Appl. https://doi.org/10.1007/978-3-642-16696-9_46
Ehsani N, Afshar A (2010) Optimization of contaminant sensor placement in water distribution networks: a multi-objective approach. In: Water distribution systems analysis 2010, pp 338–346 https://doi.org/10.1061/41203(425)32
Farhadi S, Nikoo MR, Rakhshandehroo GR, Akhbari M, Alizadeh MR (2016) An agent-based-nash modeling framework for sustainable groundwater management: a case study. Agric Water Manag 177:348–358. https://doi.org/10.1016/j.agwat.2016.08.018
Ghodsi Sh, Kerachian R, Zahmatkesh Z (2016) A multi-stakeholder framework for urban runoff quality management: Application of social choice and bargaining techniques. J Sci Total Environ 550:574–585. https://doi.org/10.1016/j.scitotenv.2016.01.052
Guth N, Klingel P (2012) In book: demand allocation in water distribution network modelling–a GIS-based approach using Voronoi diagrams with constraints. Application of Geographic Information Systems, pp 283–302. https://doi.org/10.5772/50014
Hart WE, Murray R (2010) Review of sensor placement strategies for contamination warning systems in drinking water distribution systems. J Water Resour Plann Manag 136(6):611–619. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000081
Hu C, Ren G, Liu C, Li M, Jie W (2017) A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems. Cluster Comput. https://doi.org/10.1007/s10586-017-0838-z
Janke RO, Murray R, Haxton TM, Taxon T, Bahadur R, Samuels W, Berry J, Boman E, Hart W, Riesen L, Uber J (2017) Threat ensemble vulnerability assessment-sensor placement optimization tool (TEVA-SPOT) graphical user interface user’s manual. US EPA National Homeland Security Research Center (NHSRC) 1–09. https://doi.org/10.13140/RG.2.2.18849.71521
Jiang C, Han X, Liu GR, Liu GP (2008) A nonlinear interval number programming method for uncertain optimization problems. Eur J Oper Res 188(1):1–13. https://doi.org/10.1016/j.ejor.2007.03.031
Khorshidi MS, Nikoo MR, Ebrahimi E, Sadegh M (2019) A robust decision support leader-follower framework for the design of a contamination warning system in the water distribution network. J Cleaner Prod 214:666–673. https://doi.org/10.1016/j.jclepro.2019.01.010
Khorshidi MS, Nikoo MR, Sadegh M (2018) Optimal and objective placement of sensors in water distribution systems using information theory. Water Res 143:218–228. https://doi.org/10.1016/j.watres.2018.06.050
Kim JH, Tran TVT, Chung G (2010) Optimization of water quality sensor locations in water distribution systems considering imperfect mixing. In: Water distribution systems analysis 2010, pp 317–326 https://doi.org/10.1061/41203(425)30
Klosterman S, Murray R, Szabo J, Hall J, Uber J (2010) Modeling and simulation of arsenate fate and transport in a distribution system simulator. In: Water distribution systems analysis 2010, pp 655-669https://doi.org/10.1061/41203(425)62
Lytle DA, Sorg TJ, Frietch C (2004) Accumulation of arsenic in drinking water distribution systems. Environ Sci Technol 38(20):5365–5372. https://doi.org/10.1021/es049850v
Madani K, Sheikhmohammady M, Mokhtari S, Moradi M, Xanthopoulos P (2014) Social planner’s solution for the Caspian Sea conflict. Group Decis Negot 23(3):579–596. https://doi.org/10.1007/s10726-013-9345-7,https://doi.org/10.1007/s11269-013-0279-2
Madani K, Sheikhmohammady M, Mokhtari S, Moradi M, Xanthopoulos P (2014) Social planner’s solution for the Caspian Sea conflict. Group Decis Negot 23(3):579–596. https://doi.org/10.1007/s10726-013-9345-7
Mahjouri N, Bizhani-Manzar M (2013) Waste load allocation in rivers using fallback bargaining. Water Resour Manage 27(7):2125–2136. https://doi.org/10.1007/s11269-013-0279-2
Mahjouri N, Abbasi MR (2015) Waste load allocation in rivers under uncertainty: application of social choice procedures. Environ Monit Assess 187(2):1–15. https://doi.org/10.1007/s10661-014-4194-7
Mooselu MG, Nikoo MR, Sadegh M (2019) A fuzzy multi-stakeholder socio-optimal model for water and waste load allocation. Environ Monit Assess 191(6):359. https://doi.org/10.1007/s10661-019-7504-2
Naserizade SS, Nikoo MR, Montaseri H (2018) A risk-based multi-objective model for optimal placement of sensors in the water distribution system. J Hydrol 557:147–159. https://doi.org/10.1016/j.jhydrol.2017.12.028
Nikoo MR, Kerachian R, Karimi A, Azadnia AA (2013) Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study. Environ Monit Assess 185(3):2483–2502. https://doi.org/10.1007/s10661-012-2726-6
Nikoo MR, Khorramshokouh N, Monghasemi S (2015) Optimal design of detention rockfill dams using a simulation-based optimization approach with mixed sediment in the flow. Water Resour Manage 29(15):5469–5488. https://doi.org/10.1007/s11269-015-1129-1
Nikoo MR, Beiglou PHB, Mahjouri N (2016) Optimizing multiple-pollutant waste load allocation in rivers: an interval parameter game theoretic model. Water Resour Manag 30(12):4201–4220. https://doi.org/10.1007/s11269-016-1415-6
Raei E, Alizadeh MR, Nikoo MR, Adamowski J (2019) Multi-objective decision-making for green infrastructure planning (LID-BMPs) in urban stormwater management under uncertainty. J Hydrol 579:124091. https://doi.org/10.1016/j.jhydrol.2019.124091
Raei E, Nikoo MR, Pourshahabi S (2017) A multi-objective simulation-optimization model for in-situ bioremediation of groundwater contamination: application of bargaining theory. J Hydrol 551:407–422. https://doi.org/10.1016/j.jhydrol.2017.06.010
Rasekh A, Brumbelow K (2014) Drinking water distribution systems contamination management to reduce public health impacts and system service interruptions. Environ Model Softw 51:12–25. https://doi.org/10.1016/j.envsoft.2013.09.019
Rasekh A, Brumbelow K (2015) A dynamic simulation-optimization model for adaptive management of urban water distribution system contamination threats. Appl Soft Comput 32:59–71. https://doi.org/10.1016/j.asoc.2015.03.021
Rathi S, Gupta R (2016) A simple sensor placement approach for regular monitoring and contamination detection in water distribution networks. KSCE J Civ Eng 20(2):597–608. https://doi.org/10.1007/s12205-015-0024-x
Rossman LA (2000) EPANET user’s manual, U.S. Environmental Protection Agency Risk Reduction Engineering Lab
Rossman LA, Boulos PF, Altman T (1993) DiSCTete volume-element method for network water-quality models. J Water Resour Plan Manage 119(5):505–517. https://doi.org/10.1061/(ASCE)0733-9496(1993)119:5(505)
Sankary N, Ostfeld A (2017) Incorporating operational uncertainty in early warning system design optimization for water distribution system security. Proc Eng 186:160–167. https://doi.org/10.1016/j.proeng.2017.03.222
Shafiee ME, Zechman EM (2013) An agent-based modeling framework for sociotechnical simulation of water distribution contamination events. J Hydro Inform 15(3):862–880. https://doi.org/10.2166/hydro.2013.158
Shastri Y, Diwekar U (2006) Sensor placement in water networks: a stochastic programming approach. J Water Resour Plan Manage 132(3):192–203. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:3(192)
Sheikhmohammady M, Kilgour DM, Hipel KW (2010) Modeling the Caspian Sea negotiations. Group Decis Negot 19(2):149–168. https://doi.org/10.1007/s10726-008-9121-2
Sheikhmohammady M, Madani K (2008) Bargaining over the Caspian Sea—the largest lake on the Earth. In: World environmental and water resources congress 2008, pp 1–9 https://doi.org/10.1061/40976(316)262
Shen H, McBean E (2010) Pareto optimality for sensor placements in a water distribution system. J Water Resour Plan Manage 137(3):243–248. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000111
Tavakoli A, Kerachian R, Nikoo MR, Soltani M, Estalaki SM (2014) Water and waste load allocation in rivers with emphasis on agricultural return flows application of fractional factorial analysis. Environ Monit Assess 186(9):5935–5949. https://doi.org/10.1007/s10661-014-3830-6
Weickgenannt M, Kapelan Z, Blokker M, Savic DA (2010) Risk-based sensor placement for contaminant detection in water distribution systems. J Water Resour Plan Manage 136(6):629–636. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000073
Xu J, Johnson MP, Fischbeck PS, Small MJ, VanBriesen JM (2010) Robust placement of sensors in dynamic water distribution systems. Eur J Oper Res 202(3):707–716. https://doi.org/10.1016/j.ejor.2009.06.010
Yoo DG, Chung G, Sadollah A, Kim JH (2015) Applications of network analysis and multi-objective genetic algorithm for selecting optimal water quality sensor locations in water distribution networks. KSCE J Civ Eng 19(7):23–33. https://doi.org/10.1007/s12205-015-0273-8
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zeng D, Gu L, Lian L, Guo S, Yao H, Hu J (2016) On cost-efficient sensor placement for contaminant detection in water distribution systems. IEEE Trans Ind Inf 12(6):2177–2185. https://doi.org/10.1109/TII.2016.2569413
Zhang Q, Fan Z, Pan D (1999) A ranking approach for interval numbers in uncertain multiple attribute decision-making problems. Syst Eng-Theory Pract 05
Zhao Y, Schwartz R, Salomons E, Ostfeld A, Poor HV (2016) New formulation and optimization methods for water sensor placement. Environ Model Softw 76:128–136. https://doi.org/10.1016/j.envsoft.2015.10.030
Author information
Authors and Affiliations
Corresponding author
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Naserizade, S.S., Nikoo, M.R., Montaseri, H. et al. A Hybrid Fuzzy-Probabilistic Bargaining Approach for Multi-objective Optimization of Contamination Warning Sensors in Water Distribution Systems. Group Decis Negot 30, 641–663 (2021). https://doi.org/10.1007/s10726-021-09727-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10726-021-09727-0