Skip to main content
Log in

Multi-Objective Quantity–Quality Reservoir Operation in Sudden Pollution

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Damage caused by entered pollution in reservoirs can affect a water resource system in two ways: (1) Damages that are caused due to consumption of polluted water and (2) damages that are caused due to insufficient water allocation. Those damages conflict with each other. Thus, the crisis should be managed in a way that the least damage occurs in the water resource system. This paper investigates crisis management due to the sudden entrance of a 30 m3 methyl tert-butyl ether (MTBE) load to the Karaj dam in Iran, which supplies municipal water to the cities of Tehran and Karaj. To simulate MTBE advection, dispersion, and vaporization, the latter process is added to the CE-QUAL-W2 model. After that, the multi-objective NSGAII-ALANN algorithm, which is a combination of the NSGAII optimization method along with a multi layer perceptron (MLP), which is one of the most widely used artificial neural network (ANN) structures, is employed to extract the best set of decisions in which the two aforementioned damages are minimized. By assigning a specific importance to each objective function, after extracting the optimal solutions, it is possible to choose one of the solutions with the least damage. Four scenarios of entering pollution to the Karaj reservoir the first day of each season are considered, resulting in a Pareto set of operation policies for each scenario. Results of the proposed methodology indicate that if the pollution enters the reservoir in summer, by using one of the optimal policies extracted from the Pareto set of the 2nd Scenario, by a 36 % reduction in meeting the demand, allocated pollution decreases to about 60 %. In other seasons, there is a significant decrease in allocated pollution with a smaller reduction in the met demand.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Afshar A, Shafii M, Bozorg Haddad O (2011) Optimizing multi-reservoir operation rules: an improved HBMO approach. J Hydroinf 13(1):121–139

    Article  Google Scholar 

  • Aly AH, Peralta PC (1999) Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm. Water Resour Res 35(8):2523–2532

    Article  Google Scholar 

  • Belayneh MZ, Bhallamudi SM (2012) Optimization model for management of water quality in a tidal river using upstream releases. J Water Resour Protect 4:149–162

    Article  Google Scholar 

  • Bender DA, Asher WE, Zogorsk JS (2003) A deterministic model to estimate volatile organic compound concentrations in lakes and reservoirs. U.S. Geological Survey, Open-file report, Reston, pp 03–212

    Google Scholar 

  • Bozorg Haddad O, Mariño MA (2007) Dynamic penalty function as a strategy in solving water resources combinatorial optimization problems with honey-bee optimization (HBMO) algorithm. J Hydroinf 9(3):233–250

    Article  Google Scholar 

  • Bozorg Haddad O, Adams BJ, Mariño MA (2008a) Optimum rehabilitation strategy of water distribution systems using the HBMO algorithm. J Water Supply Res Technol AQUA 57(5):327–350

    Google Scholar 

  • Bozorg Haddad O, Afshar A, Mariño MA (2008b) Design-operation of multi-hydropower reservoirs: HBMO approach. Water Resour Manag 22(12):1709–1722

    Article  Google Scholar 

  • Bozorg Haddad O, Afshar A, Mariño MA (2008c) Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs. J Hydroinf 10(3):257–264

    Article  Google Scholar 

  • Bozorg Haddad O, Afshar A, Mariño MA (2009) Optimization of non-convex water resource problems by honey-bee mating optimization (HBMO) algorithm. Eng Comput (Swansea Wales) 26(3):267–280

    Article  Google Scholar 

  • Bozorg Haddad O, Afshar A, Mariño MA (2011a) Multireservoir optimisation in discrete and continuous domains. Proc Inst Civ Eng Water Manag 164(2):57–72

    Article  Google Scholar 

  • Bozorg Haddad O, Moradi-Jalal M, Mariño MA (2011b) Design-operation optimisation of run-of-river power plants. Proc Inst Civ Eng Water Manag 164(9):463–475

    Google Scholar 

  • Castelletti A, Pianosi F, Soncini-Sessa R, Antenucci JP (2010) A multi-objective response surface approach for improved water quality planning in lakes and reservoirs. Water Resour Res 46(6). doi:10.1029/2009WR008389

  • Chaves P, Tsukatani T, Kojiri T (2004) Operation of storage reservoir for water quality by using optimization and artificial intelligence techniques. Math Comput Simul 67(4):419–432

    Article  Google Scholar 

  • Chen L, McPhee J, Yeh W (2007) A diversified multiobjective GA for optimization reservoir rule curves. Adv Water Resour 30(1):51–66

    Article  Google Scholar 

  • Cole MT, Wells AS (2006) CE-QUAL-W2: A two-dimensional, laterally averaged, hydrodynamic and water quality model, Version 3.5. U.S. Army Corps of Engineers, Washington DC

    Google Scholar 

  • Dandy G, Crawley P (1992) Optimum operation of multiple reservoir system inducing salinity effect. Water Resour Res 28(4):979–990

    Article  Google Scholar 

  • Deb K, Partap A, Agarwal S, Meyarivan T (2002) Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Dhar A, Datta B (2006) Chance constrained water quality management model for reservoir systems. ISH J Hydraul Eng 12(3):39–48

    Article  Google Scholar 

  • Dhar A, Datta B (2008) Optimal operation of reservoirs for downstream water quality control using linked simulation optimization. Hydrol Process 22(6):842–853

    Article  Google Scholar 

  • Fallah-Mehdipour E, Bozorg Haddad O, Beygi S, Mariño MA (2011) Effect of utility function curvature of Young’s bargaining method on the design of WDNs. Water Resour Manag 25(9):2197–2218

    Article  Google Scholar 

  • Fallah-Mehdipour E, Bozorg Haddad O, Mariño MA (2012) Extraction of multi-crop planning rules in a reservoir system: application of evolutionary algorithms. J Irrig Drain Eng 139(6):490–498

    Article  Google Scholar 

  • Fontane D, Labadie JW, Loftis B (1981) Optimal control of reservoir discharge quality through selective withdrawal. Water Resour Res 17(6):1594–1604

    Article  Google Scholar 

  • Ghajarnia N, Bozorg Haddad O, Mariño MA (2011) Performance of a novel hybrid algorithm in the design of water networks. Proc Inst Civ Eng Water Manag 164(4):173–191

    Article  Google Scholar 

  • Hakimi-Asiabar M, Ghodsypour H, Kerachian R (2010) Deriving operation policies for multiobjective reservoir systems: application of self-learning genetic algorithm. Appl Soft Comput 10(4):1151–1163

    Article  Google Scholar 

  • Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: From natural to artificial neural computation. Springer, Berlin, pp 195–201

  • Hayes DF, Labadie JW, Sanders TG, Brown JK (1998) Enhancing water quality in hydropower system operation. Water Resour Res 34(3):471–483

    Article  Google Scholar 

  • Heald PC, Schladow SG, Reuter JE, Allen BC (2005) Modeling MTBE and BTEX in lakes and reservoirs used for recreational boating. Environ Sci Technol 39(4):1111–1118

    Article  Google Scholar 

  • Hyduk W, Laudie H (1974) Prediction of diffusion coefficients for nonelectrolytes in dilute aqueous solutions. Am Inst Chem Eng 20(3):611–615

    Article  Google Scholar 

  • Johnson VM, Rogers LL (2000) Accuracy of neural network approximators in simulation optimization. J Water Resour Plan Manag 126(2):48–56

    Article  Google Scholar 

  • Karamouz M, Zahraie B, Kerachian R (2003) Development of a master plan for water pollution control using MCDM techniques: a case study. Water Int 28(4):478–490

    Article  Google Scholar 

  • Kerachian R, Karamouz M (2006) Optimal reservoir operation considering the water quality issue: A deterministic and stochastic conflict resolution approach. Water Resour Res 42(12):1–17

    Article  Google Scholar 

  • Kerachian R, Karamouz M (2007) A stochastic conflict resolution model for water quality management on reservoir systems. Adv Water Resour 30(4):866–882

    Article  Google Scholar 

  • Lence BJ, Takyi AK (1992) Data requirement for seasonal discharge program: an application of a regionalized sensitivity analysis. Water Resour Res 28(7):1781–1789

    Article  Google Scholar 

  • Lewis WK, Whitman WG (1924) Principles of gas absorption. Ind Eng Chem 16(12):1215–1220

    Article  Google Scholar 

  • Loftis B, Labadie JW, Fontane DG (1985) Optimal operation of a system of lakes for quality and quantity. Specialty Conference of Computer Applications in Water Resources, New York, pp 693–702

  • Moradi-Jalal M, Bozorg Haddad O, Karney BW, Mariño MA (2007) Reservoir operation in assigning optimal multi-crop irrigation areas. Agric Water Manag 90(1–2):149–159

    Article  Google Scholar 

  • Nash JF (1950) Equilibrium points in n-person games. Proc Natl Acad Sci 36(1):48–49

    Article  Google Scholar 

  • Neeklakantan TR, Pundarikanthan NV (2000) Neural network-based simulation-optimization model for reservoir operation. J Water Resour Plan Manag 126(2):57–64

    Article  Google Scholar 

  • Noory H, Liaghat AM, Parsinejad M, Bozorg Haddad O (2012) Optimizing irrigation water allocation and multicrop planning using discrete PSO algorithm. J Irrig Drain Eng 138(5):437–444

    Article  Google Scholar 

  • Rasoulzadeh-Gharibdousti S, Bozorg Haddad O, Mariño MA (2011) Optimal design and operation of pumping stations using NLP-GA. Proc Inst Civ Eng Water Manag 164(4):163–171

    Article  Google Scholar 

  • Rathbun RE (2000) Transport, behavior and fate of volatile organic compounds in stream. Environ Sci Technol 30(2):129–295

    Google Scholar 

  • Sabbaghpour S, Naghashzadehgan M, Javaherdeh K, Bozorg Haddad O (2012) HBMO algorithm for calibrating water distribution network of Langarud city. Water Sci Technol 65(9):1564–1569

    Article  Google Scholar 

  • Shirangi E, Kerachian R, Shafai Bejestan M (2008) A simplified model for reservoir operation considering the water quality issues: application of the young conflict resolution theory. Environ Monit Assess 146(1–3):77–89

    Article  Google Scholar 

  • Shokri A, Haddad OB, Mariño MA (2013) Algorithm for increasing the speed of evolutionary optimization and its accuracy in multi-objective problems. Water Resour Manag 27(7):2231–2249

    Article  Google Scholar 

  • Solomatine DP, Torres A (1996) Neural network approximation of a hydrodynamic model in optimizing reservoir operation. Proceedings of the Second International Conference on Hydroinformatics, Zurich, Switzerland

  • Soltanjalili M, Bozorg Haddad O, Mariño MA (2010) Effect of breakage level one in design of water distribution networks. Water Resour Manag 25(1):311–337

    Article  Google Scholar 

  • Soltanjalili M, Bozorg Haddad O, Seifollahi-Aghmiuni S, Mariño MA (2013) Water distribution network simulation by optimization approaches. Water Sci Technol Water Supply 13(4):1063–1079

    Article  Google Scholar 

  • Young HP (1993) An evolutionary model of bargaining. J Econ Theory 59(1):145–168

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Omid Bozorg Haddad.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shokri, A., Bozorg Haddad, O. & Mariño, M.A. Multi-Objective Quantity–Quality Reservoir Operation in Sudden Pollution. Water Resour Manage 28, 567–586 (2014). https://doi.org/10.1007/s11269-013-0504-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-013-0504-z

Keywords

Navigation