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Genetic and Evolutionary Computing pp 131–142Cite as

Spatial Evolutionary Algorithm for Large-Scale Groundwater Management

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 329))

Abstract

Large-scale groundwater management problems pose great computational challenges for decision making because of the spatial complexity and heterogeneity. This study describes a modeling framework to solve large-scale groundwater management problems using a newly developed spatial evolutionary algorithm (SEA). This method incorporates spatial patterns of the hydrological conditions to facilitate the optimal search of spatial decision variables. The SEA employs a hierarchical tree structure to represent spatial variables in a more efficient way than the data structure used by a regular EA. Furthermore, special crossover, mutation and selection operators are designed in accordance with the tree representation. In this paper, the SEA was applied to searching for the maximum vegetation coverage associated with a distributed groundwater system in an arid region. Computational experiments demonstrate the efficiency of SEA for large-scale spatial optimization problems. The extension of this algorithm for other water resources management problems.

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Author information

Authors and Affiliations

  1. Department of Forecasting, FedEx Corporate Services, 3640 Hacks Cross Rd, Memphis, TN, USA

    Jihua Wang

  2. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Ximing Cai & Albert Valocchi

Authors
  1. Jihua Wang
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  2. Ximing Cai
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  3. Albert Valocchi
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Corresponding author

Correspondence to Jihua Wang .

Editor information

Editors and Affiliations

  1. HIGH-TECH Industry Development Zone, Nanchang Institute of Technology, Jiangxi Province, China

    Hui Sun

  2. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong City, Taiwan

    Chin-Yu Yang

  3. Innovative Information Industry Research Center, Shenzhen University Harbin Institute of Technology, Shenzhen, China

    Chun-Wei Lin

  4. Harbin Institute of Technology Innovative Info. Industry Res. Center, Shenzhen University, Shenzhen, China

    Jeng-Shyang Pan

  5. Department of Computer Science Faculty of Elec. Eng. & Comp. Sci., VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  6. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

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© 2015 Springer International Publishing Switzerland

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Wang, J., Cai, X., Valocchi, A. (2015). Spatial Evolutionary Algorithm for Large-Scale Groundwater Management. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-12286-1_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12285-4

  • Online ISBN: 978-3-319-12286-1

  • eBook Packages: EngineeringEngineering (R0)

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