Skip to main content
Log in

Artificial Life Algorithm for Management of Multi-reservoir River Systems

  • Published:
Water Resources Management Aims and scope Submit manuscript

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.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • 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

    Article  Google Scholar 

  • 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):1290–1296

    Article  Google Scholar 

  • 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, USA

  • Chan Hilton AB, Culver TB (2000) Constraint handling for genetic algorithms in optimal remediation design. J Water Resour Plng Mgmt 126(3):128–137

    Article  Google Scholar 

  • Cieniawski SE, Wayland J, Ranjithan S (1995) Using genetic algorithms to solve multiobjective groundwater monitoring problem. Water Resour Res 31(2):399–409

    Article  Google Scholar 

  • 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)

  • Cunha MC, Sousa J (1999) Water distribution network design optimization: simulated annealing approach. J Water Resour Plng Mgmt 125(4):215–221

    Article  Google Scholar 

  • 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), 235–243

  • 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 Conf

  • 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):619–634

    Article  Google Scholar 

  • Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artificial Life 5(2):137–172

    Article  Google Scholar 

  • Dougherty MDE, Marryott RA (1991) Optimal groundwater management. 1. simulated annealing. Water Resour Res 27(10):2493–2503

    Article  Google Scholar 

  • Fread DL, Lewis JM (1998) The NWS FLDWAV Model. Hydrologic Research Laboratory, Department of Commerce, NOAA, NWS, Silver Spring, Maryland

  • Goldberg DE, Kuo CH (1987) Genetic algorithms in pipeline optimization. J Comput Civ Eng-ASCE 1(2):128–141

    Article  Google Scholar 

  • Hadji G, Murphy LJ (1990) Genetic algorithms for pipe network optimization. 4th Year student Civ. Engrg. Res. Rep., University of Adelaide, Adelaide, Australia

  • 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 

  • 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

    Article  Google Scholar 

  • Langton C (1989) Artificial Life. In: Artificial Life, Reading, MA: Addison-Wesley

  • Larouche B, Marche C (2008) Formulation of transfer functions flow between the hydroelectric River Peribonka. Can J Civ Eng 35(7):676–688

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • 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):200–209

    Article  Google Scholar 

  • 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, Illinois

  • Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32

    Article  Google Scholar 

  • 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, Australia

  • 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):412–432

    Article  Google Scholar 

  • Oliveira R, Loucks DP (1997) Operating rules for multi-reservoir systems. Water Resour Res 33(4):839–852

    Article  Google Scholar 

  • Savic DA, Walters GA (1997) Genetic algorithms for least cost design of water distribution networks. J Water Resour Plng Mgmt 123(2):67–77

    Article  Google Scholar 

  • Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipeoptimization. J Water Resour Plng Mgmt 120(4):423–443

    Article  Google Scholar 

  • 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):853–866

    Article  Google Scholar 

  • Sparks RE, Nelson JC, Yin Y (1998) Naturalization of the flood regime in regulated rivers. BioScience 48(9):706–720

    Article  Google Scholar 

  • Teegavarapu RSV, Simonovic SP (2002) Optimal operation of reservoir system using simulated annealing. Water Resour Manag 16(5):401–428

    Article  Google Scholar 

  • Tsakiris G, Bellos V, Ziogas C (2010) Embankement dam failure: a downstream flood hazard assessment. European Water 32:35–45

    Google Scholar 

  • 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 91–117

    Google Scholar 

  • Wang M, Zheng C (1997) Optimal groundwater management policy selection under general conditions. Ground Water 35(5):757–764

    Article  Google Scholar 

  • Wang M, Zheng C (1998) Groundwater management optimization using genetic algorithms and simulated annealing: formulation and comparison. J Am Water Resour Assoc 34(3):519–530

    Article  Google Scholar 

  • Wurbs R (1993) Reservoir-system simulation and optimization models. J Water Resour Plng Mgmt 119(4):455–472

    Article  Google Scholar 

  • Yang B, Lee Y (2000) Artificial life algorithm for function optimization. In: Proceedings of the 2000 ASME IDTEC/CIE Design and Automation Conference

  • Yang B, Lee Y, Choi B, Kim H (2001) Optimum design of short journal bearings by artificial life algorithm. Tribol Int 34(7):427–435

    Article  Google Scholar 

  • Yeh W (1985) Reservoir management and operations models: a state-of-the-art review. Water Resour Res 21(12):1797–1818

    Article  Google Scholar 

  • Young KA, Song JD, Yang B (2003) Optimal design of engine mount using an artificial life algorithm. J Sound Vibration 261:309–328

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tibebe Dessalegne.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dessalegne, T., Nicklow, J.W. Artificial Life Algorithm for Management of Multi-reservoir River Systems. Water Resour Manage 26, 1125–1141 (2012). https://doi.org/10.1007/s11269-011-9950-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-011-9950-7

Keywords

Navigation