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Soft Computing

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21.09.2021 | Application of soft computing

A new hybrid algorithm for estimating confined and leaky aquifers parameters from transient time-drawdown data

verfasst von: Walid Tadj, Mohamed Chettih, Kaddour Mouattah

Erschienen in: Soft Computing | Ausgabe 24/2021

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Abstract

One of the most common tasks of groundwater engineers is to estimate aquifer parameters from transient time-drawdown data which are measured during the execution of pumping tests. The classical method consists in the manual superimposition of the observed time-drawdown data over a type-curve, and the appreciation of the goodness-of-fit is left to the visual inspection of the engineer. The present paper proposes a computerized method based on a new hybrid algorithm named CSARao-1. It combines two recent metaheuristic algorithms: the crow search algorithm (CSA) and Rao-1 algorithm for the optimal aquifer parameters estimation, which inherits the advantages of both algorithms. The CSARao-1 hybrid algorithm along with CSA and Rao-1 algorithm was applied in conjunction with both the Theis solution to analyze time-drawdown datasets coming from confined aquifers systems and the Hantush and Jacob solution to analyze datasets coming from leaky aquifers systems. The proposed approach was coded in FORTRAN programming language and evaluated on fourteen time-drawdown datasets coming from different confined and leaky aquifers systems. The results obtained using CSARao-1 hybrid algorithm were compared to those obtained by applying CSA and Rao-1 algorithm separately, and to those recently published. Globally, the proposed CSARao-1 hybrid algorithm was found to exhibit more accuracy, better robustness and higher rate of convergence over the analyzed transient time-drawdown datasets.

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Titel: A new hybrid algorithm for estimating confined and leaky aquifers parameters from transient time-drawdown data
verfasst von: Walid Tadj
Mohamed Chettih
Kaddour Mouattah
Publikationsdatum: 21.09.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 24/2021
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-06224-z

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