Identification of the Aquifer Parameters from Pumping Test Data by Using a Hybrid Optimization Approach

8 pages•Published: September 20, 2018

Abstract

The main objective of this study is to propose a linked simulation-optimization approach to determine the parameters of the confined and leaky-confined aquifers from the results of the pumping tests. In the simulation part of the proposed approach, the drawdowns at the given monitoring points and times are calculated by considering Theis and Hantush approaches for confined and leaky-confined aquifers, respectively. This simulation part is then integrated with a hybrid optimization approach where global exploration feature of the harmony search (HS) and strong local search capability of the generalized reduced gradient (GRG) approach of the spreadsheet Solver add-in are mutually integrated. The performance of the proposed approach is evaluated by considering two pumping test data for the confined and leaky-confined aquifers. Identified results indicated that the hybrid HS-Solver optimization approach provides better results than those obtained by using both curve matching and stand-alone HS approaches.

Keyphrases: hybrid optimization, parameter estimation, pumping test

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 147-154.

Links:	https://easychair.org/publications/paper/CfQp
	https://doi.org/10.29007/11v5

BibTeX entry

@inproceedings{HIC2018:Identification_Aquifer_Parameters_from,
  author    = {M. Tamer Ayvaz and Gurhan Gurarslan},
  title     = {Identification of the Aquifer Parameters from Pumping Test Data by Using a Hybrid Optimization Approach},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/CfQp},
  doi       = {10.29007/11v5},
  pages     = {147-154},
  year      = {2018}}

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