Show simple item record

dc.contributor.authorBateni, SM
dc.contributor.authorMortazavi-Naeini, M
dc.contributor.authorAtaie-Ashtiani, B
dc.contributor.authorJeng, DS
dc.contributor.authorKhanbilvardi, R
dc.date.accessioned2017-11-28T23:38:30Z
dc.date.available2017-11-28T23:38:30Z
dc.date.issued2015
dc.identifier.issn1568-4946
dc.identifier.doi10.1016/j.asoc.2014.12.022
dc.identifier.urihttp://hdl.handle.net/10072/141704
dc.description.abstractAn accurate estimation of aquifer hydraulic parameters is required for groundwater modeling and proper management of vital groundwater resources. In situ measurements of aquifer hydraulic parameters are expensive and difficult. Traditionally, these parameters have been estimated by graphical methods that are approximate and time-consuming. As a result, nonlinear programming (NLP) techniques have been used extensively to estimate them. Despite the outperformance of NLP approaches over graphical methods, they tend to converge to local minima and typically suffer from a convergence problem. In this study, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) methods are used to identify hydraulic parameters (i.e., storage coefficient, hydraulic conductivity, transmissivity, specific yield, and leakage factor) of three types of aquifers namely, confined, unconfined, and leaky from real time–drawdown pumping test data. The performance of GA and ACO is also compared with that of graphical and NLP techniques. The results show that both GA and ACO are efficient, robust, and reliable for estimating various aquifer hydraulic parameters from the time–drawdown data and perform better than the graphical and NLP techniques. The outcomes also indicate that the accuracy of GA and ACO is comparable. Comparing the running time of various utilized methods illustrates that ACO converges to the optimal solution faster than other techniques, while the graphical method has the highest running time.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherElsevier BV
dc.publisher.placeNetherlands
dc.relation.ispartofpagefrom541
dc.relation.ispartofpageto549
dc.relation.ispartofjournalApplied Soft Computing Journal
dc.relation.ispartofvolume28
dc.subject.fieldofresearchCivil Geotechnical Engineering
dc.subject.fieldofresearchApplied Mathematics
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchcode090501
dc.subject.fieldofresearchcode0102
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0806
dc.titleEvaluation of methods for estimating aquifer hydraulic parameters
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorJeng, Dong-Sheng


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record