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Confined Aquifer’s Hydraulic Parameters Estimation by a Generalized Regression Neural Network

  • Atefeh Delnaz
  • Gholamreza RakhshandehrooEmail author
  • Mohammad Reza Nikoo
Research Paper
  • 9 Downloads

Abstract

Precise estimation of aquifer hydraulic parameters can be an asset to more sustainable management of groundwater resources. The nonlinear correlation of aquifer parameters along with equation-level complicacy of aquifers’ charging functions can be efficiently handled using artificial intelligence models due to their flexibility in mapping between the observed data and output functions. This study focuses on performance comparisons for generalized regression neural network, artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) for estimation of hydraulic parameters, namely storage coefficient and transmissibility of confined aquifers. To acquire a greater precision of the estimated output functions, the principle component analysis of the pumping test data was initially undertaken to reduce the input dimensions by filtering out redundancies and insignificant variables’ correlations. Next, these data are passed into the training and validation process of the artificial intelligence models. Several error indices, mean absolute relative error (MARE), RMSE, MAE, RMRE, Bias and R2 were used during the validation of the prediction models. Finally, the estimated output functions were compared against the traditional and yet commonly used graphical Theis method. As an example, MARE in aquifer parameters estimation by ANN and graphical Theis method was 0.5564% and 1.1320%, respectively. More generally, among all the intelligence prediction models used to estimate the hydraulic parameters of a confined aquifer, ANFIS was more accurate and sensibly required much less computational time than the others and, hence, may be selected as the superior model in aquifer parameters estimation.

Keywords

Groundwater management Aquifer parameter estimation Artificial intelligence models Artificial neural network Generalized regression neural network 

Notes

Compliance with Ethical Standards

Conflict of interest

Authors declare that they have no conflict of interest in this research.

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Copyright information

© Shiraz University 2019

Authors and Affiliations

  • Atefeh Delnaz
    • 1
  • Gholamreza Rakhshandehroo
    • 1
    Email author
  • Mohammad Reza Nikoo
    • 1
  1. 1.Department of Civil and Environmental EngineeringShiraz UniversityShirazIran

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