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Journal of Porous Media
IF: 1.061 5-Year IF: 1.151 SJR: 0.504 SNIP: 0.671 CiteScore™: 1.58

ISSN Print: 1091-028X
ISSN Online: 1934-0508

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Journal of Porous Media

DOI: 10.1615/JPorMedia.v9.i5.40
pages 429-444

Forecasting Aquifer Parameters Using Artificial Neural Networks

Halil KARAHAN
Pamukkale University
M. Tamer Ayvaz
Pamukkale University, Faculty of Engineering, Department of Civil Engineering, 20017 Denizli, Turkey

ABSTRACT

This study proposes an artificial neural network (ANN) model to solve an inverse parameter identification problem for groundwater modeling. It is a problem for which the transmissivities can be obtained for given hydraulic heads. ANN may be a useful tool for parameter estimation problems because of its ability to model complex nonlinear relationships between state variables and system parameters without a priori assumptions of the nature of a relationship like a black box. To carry out a parameter estimation using the ANN, a hypothetical example has been examined under two scenarios, one involving the sink and/or source terms, the second without these. In the ANN model, the network is trained for about 5, 10, and 20% of all data, and then transmissivities in the other cells are forecasted. Results show that observed and forecasted transmissivities are in good agreement when about 10 and 20% of the hydraulic heads in the solution domain are known.


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