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

Tadj, Walid; Chettih, Mohamed; Mouattah, Kaddour

doi:10.1007/s00500-021-06224-z

Cited by 5 publications

(1 citation statement)

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“…Recently, an increasing trend in the use of machine learning methods [22,23], artificial neural networks [24,25], fuzzy logic [26,27], genetic algorithms [28,29], and hybrid machine learning methods [30,31] has been observed. A good and updated review about pumping tests can be found in [32], with a complete bibliography to refer to for further information.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic Characterization of Carbonate Aquifers Using Atypical Pumping Tests without the Interruption of the Drinking Water Supply

Rusi,

Di Curzio,

Di Giovanni

2024

Water

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The Gran Sasso carbonate aquifer is the largest and most productive in the Apennines. Its hydrogeological structure has been studied since the middle of the last century for the springs’ characterization for drinking purposes and for a motorway tunnel. Meanwhile, its hydrodynamic parametrization is less developed and has been limited to monitoring the discharge and chemical and isotopic parameters. Secondary porosity characterizes the aquifer, and an underlying impermeable marly complex represents the basal aquiclude. It might appear inappropriate to characterize the hydraulic properties via pumping tests, as their reliability has been proven in homogeneous and isotropic media. However, the high extent of the aquifer, the wells’ location, the scarcity of information available and the lack of alternatives has forced the estimation of hydrodynamic parameters as in porous aquifers and the experimental testing of the aquifer, especially in maximum pumping conditions, for a possible exploitation increase. Since aquifer testing was performed during the normal well field’s activities, it was not possible to perform typical tests. Therefore, the step-drawdown test was conducted by turning on an increasing number of wells over time and keeping the observation points fixed. As results, a mean hydraulic conductivity of 5 × 10−3 m/s and a mean transmissivity of 0.3 m2/s were established without interrupting the water supply; meanwhile, the influence radius and flow directions were also estimated.

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