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Kharazmi university , t.azari@khu.ac.ir
Abstract: (288 Views)
Accurate determination of hydraulic parameter values is the first step to the sustainable development of an aquifer. Since Theis (1935), type curve matching technique (TCMT) has been used to estimate the aquifer parameters from pumping test data. The TCMT is subjected to graphical error. To eliminate the error a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) is developed as an alternative to the conventional TCMT by modeling the Bourdet-Gringaten’s well function for the determination of the fractured double porosity aquifer parameters. The MLP model is developed in a four step protocol and is trained for the well function of double porosity aquifers by the back propagation method and the Levenberg-Marquardt optimization algorithm. By applying the principal component analysis (PCA) on the training input data and through a trial and error procedure the optimum structure of the network is fixed with the topology of [3×6×3]. The validity of the developed network are evaluated with synthetic and real field data. The network receives pumping test data and provides the user with the aquifer parameter values. The developed network provides an automatic and fast procedure for the double porosity aquifer parameter determination that eliminates graphical errors inherent in the conventional TCMT.
Keywords: Aquifer parameters, Double porosity, Well function, Multi Layaer Perceptron (MLP), Principal Component Analysis (PCA).
Type of Study: Original Research |
Subject:
Hydrogeology
Received: 2025/06/10 | Accepted: 2025/08/4 | Published: 2025/08/27
Received: 2025/06/10 | Accepted: 2025/08/4 | Published: 2025/08/27
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