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Prediction of sediment transportation in deep bay (Hong Kong) using genetic algorithm

  • Environmental Hydrodynamics
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Abstract

The genetic algorithm (GA) is a powerful method which can be used to solve search and optimization problems. A genetic algorithm with tournament selection, uniform crossover and uniform mutation is used to optimize sediment transport parameters in this study. Two important parameters of sediment transport, the critical shear stress for deposition and resuspension, are optimized by GA. The results show that GA is efficient and robust for optimizing parameters of our sediment transport simulation of Deep Bay.

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References

  1. Ariathurai R, Krone R B. Finite element model for cohesive sediment transport [J]. Journal of Hydraulic Engineering Division, ASCE, 1976, 102: 323–338.

    Google Scholar 

  2. Cole P, Miles G V. Two-dimensional model of mud transport [J]. Journal of Hydraulic Engineering Division, ASCE, 1983, 109: 1–12.

    Article  Google Scholar 

  3. Hayter E J, Mehta A J. Modelling cohesive sediment transport in estuarine waters [J]. Applied Mathematical modeling, 1986, 10: 294–303.

    Article  Google Scholar 

  4. Van Rijn L C. Mathematical modeling for suspended sediment in nonuniform flows [J]. Journal of Hydraulic Engineering, ASCE, 1986, 112(6): 433–455.

    Article  Google Scholar 

  5. Katopodi I, Ribberink J S. Quasi-3D modeling of suspended sediment transport by currents and waves [J]. Coastal Engineering, 1992, 18: 83–110.

    Article  Google Scholar 

  6. Holland J H. Adaptation in Natural and Artificial Systems [M]. The University of Michigan Press, 1975.

  7. Goldberg D E. Genetic Algorithms in Search Optimization and Machine Learning [M]. Addison-Wesley Publishing Company, 1989.

  8. Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs [M]. Springer-Verlag, 1999.

  9. Wang Q J. The genetic algorithm and its application to calibrating conceptual rainfall-runoff models [J]. Water Resources Research, 1991, 27(9): 2467–2471.

    Article  Google Scholar 

  10. Mohan S, Loucks D P. Genetic algorithms for estimat-ing estimating model parameters [J]. Integrated Water Resour. Plng. for the 21 st Century, Proc. of the 22 nd Annu. Conf., ASCE, 1995

  11. McKinney D C, Min-Der Lin. Genetic algorithm solution of groundwater management models [J]. Water Resou. Res., 1994, 30(6): 1897–1906.

    Article  Google Scholar 

  12. Vasques J A, Mailer H R, Lence B J, et al. Achieving water quality system reliability using genetic algorithms [J]. Journal of Environmental Engineering, 2000, 126(10): 954–962.

    Article  Google Scholar 

  13. Mulligan A E. Genetic algorithms for calibrating water quality models. MS thesis, Medford, Department of Civil and Environmental engineering, Tufts University.

  14. Cai X, Mckinney D C, Lasdon L S. Solving nonlinear water management models using a combined genetic algorithm and linear programming approach [J]. Adv Water Resour, 2001, 24(6): 667–676.

    Article  Google Scholar 

  15. Khu S T, F di Pierro, Savic D, et al. Incorporating spatial and temporal information for urban drainage model calibration: An approach using preference ordering genetic algorithm [J]. Advances in Water Resource, 2006, 29: 1168–1181.

    Article  Google Scholar 

  16. Holland J H. Concerning efficient adaptive systems Self-organizing Systems. edited by Yovits M.C., Jacohi G.T., Goldstein G.D. (Washington, DC: Spartan Books), 1962: 215–230.

  17. Holland J H. Information processing in adaptive systems, Information Processing in the Nervous System [C]. Proceedings of the International Union of Physiological Sciences, 1962, 3: 330–339.

    Google Scholar 

  18. Holland J H. Some practical aspects of adaptive systems theory. Electronic Information Handling, edited by Kent, A., Taulhee, O.E. (Washington, DC: Spartan Books), 209–217.

  19. Bagley J D. The behavior of adaptive systems which employ genetic and correlation algorithms. Ph. D. Thesis, Michigan, The University of Michigan, 1967.

    Google Scholar 

  20. De Jong K A. An analysis of the behavior of a class of genetic adaptive systems. Ph. D. Thesis, Michigan, The University of Michigan, 1975.

    Google Scholar 

  21. Krone R B. Flume studies of the transport of sediment in estuarial processes. Final Report. Rep. of Hydraulic Engineering Laboratory and Sanitary Engineering Research Laboratory, 1962, Berkeley, University of California

    Google Scholar 

  22. Lick W. Entrainment deposition and transport of fine-grained sediments in lakes [J]. Hydrobiologia, 1982, 91: 31–40.

    Article  Google Scholar 

  23. Thorn M F C, Parsons J G. Properties of Grangemouth Mud [J]. Hydraulic Research Station Rep. No. EX 781, 1977, Wallingford UK.

  24. Liu W C, Hsu M H, Kuo A Y. Modelling of hydrodynamics and cohesive sediment transport in Tashui River estuarine system, Taiwan [J]. Marine Pollution Bulletin, 2002, 44: 1076–1088.

    Article  Google Scholar 

  25. Wu Y, Falconer R A, Uncles R J. Modelling of water flows and cohesive sediment fluxes in the Huber estuary, UK [J]. Marine Pollution Bulletin, 1998, 37(3–7): 182–189.

    Google Scholar 

  26. Muhlenbein H. Parallel genetic algorithms population genetics and combinatorial optimization [C]. Proceedings of the Third International Conference on Genetic Algorithms, 1989: 416–421.

  27. Baker J E. Adaptive selection methods for genetic algorithms [C]. Proceedings of an International Conference on Genetic Algorithms and Their Applications, 1985: 100–111.

  28. Suh J Y, Van Guncht D. Distributed genetic algorithms. (Technical Report No. 225), 1987, Bloomington: Indiana University, Computer Science Department.

    Google Scholar 

  29. Wardlaw R, Sharif M. Evaluation of genetic algorithms for optimal reservoir system operation [J]. Water Resour. Plan. Manage, 1999, 125(1): 25–33.

    Article  Google Scholar 

  30. Soremekun G, Gurdal Z, Haftka R T, et al. Composite laminate design optimization be genetic algorithm with generalized elitist selection [J]. Comp Struct, 2001, 79: 131–143.

    Article  Google Scholar 

  31. Spears W, DeJong K. On the virtues of parametrized uniform crossover [C]. Proceedings of the Fourth International Conference on Genetic Aogorithms, 1991.

  32. Syswerda G. Uniform crossover in genetic algorithms [C]. Proceedings of the Third International Conference on Genetic Algorithms, 1989

  33. Hu X J, Di Paolo E. An efficient genetic algorithm with uniform crossover for air traffic control [J]. Computers & Operations Research, 2009, 36: 245–259.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil & Structural Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    F. X. Zhang & Onyx W. H. Wai

  2. Department of Ocean Environment, University of Xiamen, Xiamen, China

    Y. W. Jiang

Authors
  1. F. X. Zhang
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  2. Onyx W. H. Wai
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  3. Y. W. Jiang
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Correspondence to F. X. Zhang.

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Zhang, F.X., Wai, O.W.H. & Jiang, Y.W. Prediction of sediment transportation in deep bay (Hong Kong) using genetic algorithm. J Hydrodyn 22 (Suppl 1), 582–587 (2010). https://doi.org/10.1016/S1001-6058(09)60260-2

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