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Groundwater Model Calibration by Meta-Heuristic Algorithms

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  • O. Haddad
  • M. Tabari
  • E. Fallah-Mehdipour
  • M. Mariño

Abstract

Groundwater models are computer models that simulate or predict aquifer conditions by using input data sets and hydraulic parameters. Commonly, hydraulic parameters are extracted by calibration, using observed and simulated aquifer conditions. The accuracy of calibration affects other modeling processes, especially the hydraulic head simulation. Meta-heuristic algorithms are good candidates to determine optimal/near-optimal parameters in groundwater models. In this paper, two meta-heuristic algorithms: (1) particle swarm optimization (PSO) and (2) pattern search (PS) are applied and compared in the Ghaen aquifer, by considering the sum of the squared deviation (SSD) between observed and simulated hydraulic heads and the sum of the absolute value of deviation (SAD) between observed and simulated hydraulic heads as the objective functions. Results show that obtained values of the objective function are enhanced significantly by using the PS algorithm. Accordingly, PS improves (decreases) the SSD and SAD by 0.20 and 2.36 percent, respectively, compared to results reported by using the PSO algorithm. Results also indicate that the proposed PS optimization tool is effective in the calibration of aquifer parameters. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • O. Haddad & M. Tabari & E. Fallah-Mehdipour & M. Mariño, 2013. "Groundwater Model Calibration by Meta-Heuristic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2515-2529, May.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:7:p:2515-2529
    DOI: 10.1007/s11269-013-0300-9
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    References listed on IDEAS

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