Advertisement

Water Resources Management

, Volume 26, Issue 5, pp 1125–1141 | Cite as

Artificial Life Algorithm for Management of Multi-reservoir River Systems

  • Tibebe Dessalegne
  • John W. Nicklow
Article

Abstract

The design and operation of civil engineering systems, particularly water resources systems, has been pursued from the perspective of minimizing costs and related negative impacts, maximizing benefits, or a combination thereof. Due to the complex, nonlinear nature of the majority of systems, together with an increase in digital computing capabilities, global search algorithms are becoming a common means of meeting these objectives. This paper employs an artificial life algorithm, derived from the artificial life paradigm. The algorithm is evaluated using standard optimization test functions and is subsequently applied to determine optimal dam operations in multi-reservoir river systems. The optimal dam operation scheme is that which indirectly minimizes environmental impacts caused by short-term water level fluctuations. Optimal releases are sought by coupling an artificial life algorithm with FLDWAV, a one-dimensional, steady flow simulation model. The resulting multi-reservoir management model is successfully applied to a portion of the Illinois River Waterway.

Keywords

Artificial life algorithm Optimization Water level fluctuation Cluster analysis Illinois River Evolutionary algorithm 

References

  1. Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. Water Resour Manag 19(2):145–161. doi: 10.1007/s11269-005-2704-7 CrossRefGoogle Scholar
  2. Anderson BG, Rutherfurd ID, Western AW (2006) An analysis of the influence of riparian vegetation on the propagation of flood waves. Environ Model Softw 21(9):1290–1296CrossRefGoogle Scholar
  3. Assad MA, Packard NH (1993) Emergent colonization in an artificial ecology, toward a practice of autonomous systems. In: Proceedings of the First European Conference on Artificial Life, 143–152. MIT Press, Cambridge, MA, USAGoogle Scholar
  4. Chan Hilton AB, Culver TB (2000) Constraint handling for genetic algorithms in optimal remediation design. J Water Resour Plng Mgmt 126(3):128–137CrossRefGoogle Scholar
  5. Cieniawski SE, Wayland J, Ranjithan S (1995) Using genetic algorithms to solve multiobjective groundwater monitoring problem. Water Resour Res 31(2):399–409CrossRefGoogle Scholar
  6. Coello Coello CA (1999) A survey of constraint handling techniques used with evolutionary algorithms. Technical report: Lania-RI-99-04, Laboratorio Nacional de Informatica Avanzada (Mexico)Google Scholar
  7. Cunha MC, Sousa J (1999) Water distribution network design optimization: simulated annealing approach. J Water Resour Plng Mgmt 125(4):215–221CrossRefGoogle Scholar
  8. Deb K, Agrawal S (1999) A niched-penalty approach for constraint handling in genetic algorithms. In: Proceedings of the Fourth International Conference on Neural Networks and Genetic Algorithms (ICANNGA 99), 235–243Google Scholar
  9. Demissie M, Xia RK, Knapp HV (1999) Significance of water level fluctuation management in the restoration of large rivers. In: Proceedings of the 1999 International Water Resources Engineering ConfGoogle Scholar
  10. Dessalegne T, Nicklow JW, Minder E (2004) Evolutionary computation to control unnatural water level fluctuations in multi-reservoir river systems. River Res Appl 20(6):619–634CrossRefGoogle Scholar
  11. Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artificial Life 5(2):137–172CrossRefGoogle Scholar
  12. Dougherty MDE, Marryott RA (1991) Optimal groundwater management. 1. simulated annealing. Water Resour Res 27(10):2493–2503CrossRefGoogle Scholar
  13. Fread DL, Lewis JM (1998) The NWS FLDWAV Model. Hydrologic Research Laboratory, Department of Commerce, NOAA, NWS, Silver Spring, MarylandGoogle Scholar
  14. Goldberg DE, Kuo CH (1987) Genetic algorithms in pipeline optimization. J Comput Civ Eng-ASCE 1(2):128–141CrossRefGoogle Scholar
  15. Hadji G, Murphy LJ (1990) Genetic algorithms for pipe network optimization. 4th Year student Civ. Engrg. Res. Rep., University of Adelaide, Adelaide, AustraliaGoogle Scholar
  16. Hayashi D, Satoh T, Okita D (1996) Distributed optimization by using artificial life. Trans IEE Japan 116-C(5):584–590 (in Japanese)Google Scholar
  17. Jothiprakash V, Shanthi G (2008) Comparison of policies derived from stochastic dynamic programming and genetic algorithm models. Water Resour Manag 23:1563–1580. doi: 10.1007/s11269-006-9143-y CrossRefGoogle Scholar
  18. Langton C (1989) Artificial Life. In: Artificial Life, Reading, MA: Addison-WesleyGoogle Scholar
  19. Larouche B, Marche C (2008) Formulation of transfer functions flow between the hydroelectric River Peribonka. Can J Civ Eng 35(7):676–688CrossRefGoogle Scholar
  20. Li Y, Chan Hilton AB, Tong L (2004) Development of ant colony optimization for long-term groundwater monitoring. In: Proc., World Water and Environmental Resources Congress, ASCE Google Scholar
  21. Madadgar S, Afshar A (2009) An improved continuous ant algorithm for optimization of water resources Problems. Water Resour Manage 23:2119–2139. doi: 10.1007/s11269-008-9373-2 CrossRefGoogle Scholar
  22. Maier HR, Simpson AR, Zecchin AC, Foong WK, Phang KY, Seah HY, Tan CL (2003) Ant colony optimization for the design of water distribution systems. J Water Resour Plng Mgmt 129(3):200–209CrossRefGoogle Scholar
  23. Meyer PD, Eheart JW, Ranjithan S, Valocchi AJ (1992) Groundwater monitoring network design at hazardous waste disposal facilities under conditions of uncertainty. Proj. Rep. 91–061, Hazard. Waste Res. and Inf. Cent., Univ. of Ill. at Urbana-Champaign, Urbana, IllinoisGoogle Scholar
  24. Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32CrossRefGoogle Scholar
  25. Murphy LJ, Simpson AR (1992) Genetic algorithms in pipe network optimization. Res. Rep. No. R93, Dept. of Civ. and Envir. Engrg., University of Adelaide, Adelaide, AustraliaGoogle Scholar
  26. Nicklow JW, Reed P, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, Karamouz M, Minsker B, Ostfeld A, Singh A, Zechman E (2010) State of the art for genetic algorithms and beyond in water resources planning and management. J Water Resour Plng Manag, ASCE 136(4):412–432CrossRefGoogle Scholar
  27. Oliveira R, Loucks DP (1997) Operating rules for multi-reservoir systems. Water Resour Res 33(4):839–852CrossRefGoogle Scholar
  28. Savic DA, Walters GA (1997) Genetic algorithms for least cost design of water distribution networks. J Water Resour Plng Mgmt 123(2):67–77CrossRefGoogle Scholar
  29. Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipeoptimization. J Water Resour Plng Mgmt 120(4):423–443CrossRefGoogle Scholar
  30. Skaggs RL, Mays LW, Vail LW (2001) Simulated annealing with memory and directional search for groundwater remediation design. J American Water Res Assoc 37(4):853–866CrossRefGoogle Scholar
  31. Sparks RE, Nelson JC, Yin Y (1998) Naturalization of the flood regime in regulated rivers. BioScience 48(9):706–720CrossRefGoogle Scholar
  32. Teegavarapu RSV, Simonovic SP (2002) Optimal operation of reservoir system using simulated annealing. Water Resour Manag 16(5):401–428CrossRefGoogle Scholar
  33. Tsakiris G, Bellos V, Ziogas C (2010) Embankement dam failure: a downstream flood hazard assessment. European Water 32:35–45Google Scholar
  34. Walters GA, Cembrowicz RG (1993) Optimal design of water distribution networks. In: Cabrera E, Martinez F (eds) Water supply systems, state-of-the-art and future trends. Computational Mechanics Inc., Southampton, pp 91–117Google Scholar
  35. Wang M, Zheng C (1997) Optimal groundwater management policy selection under general conditions. Ground Water 35(5):757–764CrossRefGoogle Scholar
  36. Wang M, Zheng C (1998) Groundwater management optimization using genetic algorithms and simulated annealing: formulation and comparison. J Am Water Resour Assoc 34(3):519–530CrossRefGoogle Scholar
  37. Wurbs R (1993) Reservoir-system simulation and optimization models. J Water Resour Plng Mgmt 119(4):455–472CrossRefGoogle Scholar
  38. Yang B, Lee Y (2000) Artificial life algorithm for function optimization. In: Proceedings of the 2000 ASME IDTEC/CIE Design and Automation ConferenceGoogle Scholar
  39. Yang B, Lee Y, Choi B, Kim H (2001) Optimum design of short journal bearings by artificial life algorithm. Tribol Int 34(7):427–435CrossRefGoogle Scholar
  40. Yeh W (1985) Reservoir management and operations models: a state-of-the-art review. Water Resour Res 21(12):1797–1818CrossRefGoogle Scholar
  41. Young KA, Song JD, Yang B (2003) Optimal design of engine mount using an artificial life algorithm. J Sound Vibration 261:309–328CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.South Florida Water Management DistrictWest Palm BeachUSA
  2. 2.Department of Civil and Environmental EngineeringSouthern Illinois University at CarbondaleCarbondaleUSA

Personalised recommendations