Advertisement

Water Resources Management

, Volume 27, Issue 7, pp 1931–1947 | Cite as

Multiobjective Calibration of Reservoir Water Quality Modeling Using Multiobjective Particle Swarm Optimization (MOPSO)

  • Abbas AfsharEmail author
  • Nasim Shojaei
  • Mahdi Sagharjooghifarahani
Article

Abstract

Water resource management encounters large variety of multi objective problems that require powerful optimization tools in order to fully characterize the existing tradeoffs between various objectives that can be minimizing difference between forecasted physical, chemical, and biological behaviors of model and measured data. Calibration of complex water quality models for river and reservoir systems may include conflicting objectives addressed by various combinations of interacting calibration parameters. Calibration of the two dimensional CE-QUAL-W2 water quality and hydrodynamic model is an excellent example where the model must be calibrated for both hydrodynamic and water quality behavior. The aim of the present study is to show how multiobjective particle swarm optimization (MOPSO) can be implemented for automatic calibration of water quality and hydrodynamic parameters of a 2-dimensional, hydrodynamic, and water quality models (CEQUAL-W2) to predict physical, chemical, and biological behaviors of a water body, and then focus on a relevant case study. So MOPSO is utilized to generate Pareto optimal solutions for two conflicting calibration objectives. A combined measure of thermal and reservoir water level is considered as the first calibration objective. The second objective is formulated to forecast the best physical, chemical, and biological behavior of the model. Realizing the strong interactions between water quality and hydrodynamic issues of water bodies and their dependencies on the same set of calibration parameters, the proposed multiobjective approach may provide a wide version of all possible calibration solutions for better decision making to select best solution from pareto front.

Keywords

Multiobjective particle swarm optimization algorithm Multiobjective calibration Water quality modeling Karkheh Reservoir CE-QUAL-W2 

References

  1. Afshar MH (2012) Large scale reservoir operation by constrained Particle Swarm Optimization algorithms. J Hydro Environ Res 6(1):75–87CrossRefGoogle Scholar
  2. Afshar A, Saadatpour M (2009) Reservoir eutrophication modeling, sensitivity analysis, and assessment; application to Karkheh Reservoir, Iran. J Environ Eng Sci 26(7):1227–1238. doi: 10.1089/ees.2008.0319 CrossRefGoogle Scholar
  3. Afshar A, Kazemi H, Saadatpour M (2011) Particle swarm optimization for automatic calibration of large scale water quality model (CE-QUAL-W2): application to Karkheh reservoir, Iran. Water Resour Manag 25(1):2613–2632CrossRefGoogle Scholar
  4. Baker T, Dycus D (2004) Use of monitoring information to identify and implement water quality improvement. National monitoring conference, Chattanooga, TN 17–20 MayGoogle Scholar
  5. Bozorg Haddad O, Afshar A, Mariño MA (2006) Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resour Manag 20(5):661–680CrossRefGoogle Scholar
  6. Chang FJ, Chen L, Chang LC (2005) Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol Process 19(11):2277–2289. doi: 10.1002/hyp.5674 CrossRefGoogle Scholar
  7. Chapra SC (1997) Surface water quality modeling. McGraw-Hill Co. Inc, SingaporeGoogle Scholar
  8. Chung SW, Oh JK (2006) Calibration of CE-QUAL-W2 for a monomictic reservoir in a monsoon climate area. Water Sci Technol 54(11–12):29–37. doi: 10.2166/wst.2006.841 Google Scholar
  9. Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRefGoogle Scholar
  10. Coello Coello CA, Pulido GT, Lechuga MS (2004) Handling multiple objective with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279CrossRefGoogle Scholar
  11. Cole TM, Wells SA (2000) CE-QUAl-W2: a two-dimensional, laterally averaged, hydrodynamic and water quality model, version 3.0. Instruction Report EL-2000. US Army Engineering and Research Development Center, VicksburgGoogle Scholar
  12. Confesor RB Jr, Whittaker GW (2007) Automatic calibration of hydrologic models with multiobjective evolutionary algorithm and pareto optimization. J Am Water Resour Assoc (JAWRA) 43(4):981–989CrossRefGoogle Scholar
  13. Deb K (2001) Multi-objective optimization using evolutionary algorithms. John Wiley, New YorkGoogle Scholar
  14. Deb K, Goldberg DE (1989) An investigation of niche and species formation in genetic function optimization. In: Schaffer JD (ed) Proceedings of 3rd international conference genetic algorithms, San Mateo, CA, 42–50Google Scholar
  15. Dehdari V, Oliver DS, Deutsch CV (2012) Comparison of optimization algorithms for reservoir management with constraints—a case study. J Pet Sci Eng 100:41–49CrossRefGoogle Scholar
  16. Deo MC (2011) Application of data driven methods in hydrology and hydraulics. International Conference on Managing River in the 21st century, Penang, Malaysia, 6–9 December 2011Google Scholar
  17. Diogo PA, Fonseca M, Coelho PS, Mateus NS, Almeida MC, Rodrigues AC (2008) Reservoir phosphorous sources evaluation and water quality modeling in a transboundary watershed. Desalination, 10th IWA International Specialized Conference on Diffuse Pollution and Sustainable Basin Management, Istanbul, Turkey 226:200–214Google Scholar
  18. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. Proceedings Sixth Symposium on Micro Machine and Human Science, Nagoya Japan 39–43Google Scholar
  19. Eckhardt K, Arnold JG (2001) Automatic calibration of a distributed catchment model. J Hydrol 251(1–2):103–109CrossRefGoogle Scholar
  20. Etemad-Shahidi A, Afshar A, Alikia H, Moshfeghi H (2009) Total dissolved solid modeling; Karkheh reservoir case example. Int J Environ Res 3:671–680Google Scholar
  21. Fallah-Mehdipour E, Bozorg Haddad O, Mariño MA (2011) MOPSO algorithm and its application in multipurpose multi reservoir operation. J Hydroinformatics 13(4):794–811CrossRefGoogle Scholar
  22. Gelda RK, Effler SW (2007) Testing and application of a two-dimensional hydrothermal model for a water supply reservoir: implication of sedimentation. Environ Eng Sci 6(1):73–84CrossRefGoogle Scholar
  23. Hayes DF, Labadie JW, Sanders TG, Brown JK (1998) Enhancing water quality in hydropower system operations. Water Resour Res 34(3):471–483. doi: 10.1029/97WR03038 CrossRefGoogle Scholar
  24. Iran Water and Power Company (2006) Technical and research report, phase I, reservoir eutrophication, modelling and management; application to Karkheh ReservoirGoogle Scholar
  25. Isazadeh S, Tajrish M, Abrishamchi A, Ahmadi M (2005) Application of phosphorous simulation models to latian reservoir. J Water Wastewater 54:3–16Google Scholar
  26. Jalali MR, Afshar A, Mariño MA (2007) Multi-colony ant algorithm for continuous multi-reservoir operation optimization problem. Water Resour Manag 21(9):1429–1447CrossRefGoogle Scholar
  27. Kuo JT, Lung WS, Yang CP, Liu WC, Yang MD, Tang TS (2006a) Eutrophication modeling of reservoirs in Taiwan. Environ Model Softw 21:829–844CrossRefGoogle Scholar
  28. Kuo JT, Wang YY, Lung WS (2006b) A Hybrid neural-genetic algorithm for reservoir water quality management. Water Res 40(7):1367–1376CrossRefGoogle Scholar
  29. Kurek W, Ostfeld A (2013) Multi-objective optimization of water quality, pumps operation, and storage sizing of water distribution systems. J Environ Manage 115:189–197CrossRefGoogle Scholar
  30. Liu WC, Chen WB, Kimura N (2008) Impact of phosphorus load reduction on water quality in a stratified reservoir-eutrophication modeling study. Environ Monit Assess 159:393–406CrossRefGoogle Scholar
  31. Madsen H (2000) Automatic calibration of a conceptual rainfall–runoff model using multiple objectives. J Hydrol 235:276–288CrossRefGoogle Scholar
  32. Mahinthakumar G, Sayeed M (2005) Hybrid genetic algorithm-local search methods for solving groundwater source identification inverse problems. J Water Resour Plan Manag 131(1):45–57CrossRefGoogle Scholar
  33. Mohamadi H (2002) Two dimentional reservoir eutrophication modelling. M.Sc. thesis, Iran University of Science and TechnologyGoogle Scholar
  34. Nielsen EJ (2005) Algal succession and nutrient dynamics in elephant butte reservoir. M.Sc. thesis, Brigham Young UniversityGoogle Scholar
  35. Sullivan AB, Round SA (2005) Modeling hydrodynamics, temperature, and water quality in Henry Hagg Lake, Oregon, 2000–3. http://pubs.usgs.gov/sir/2004/5261
  36. Yongtai H, Lei L (2010) Multiobjective water quality model calibration using a hybrid genetic algorithm and neural network-based approach. J Environ Eng 136(10):1020–1031CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Abbas Afshar
    • 1
    Email author
  • Nasim Shojaei
    • 2
    • 3
  • Mahdi Sagharjooghifarahani
    • 2
    • 3
  1. 1.Department of Civil EngineeringIran University of Science and TechnologyTehranIran
  2. 2.Department of Civil EngineeringPortland State UniversityPortlandUSA
  3. 3.Maseeh College of Engineering and Computer ScienceThe Northwest Center for Engineering, Science and Technology (Engineering Building)PortlandUSA

Personalised recommendations