Determination the Hydrodynamic Parameters of Confined Aquifer Using Sugeno Fuzzy Logic

Document Type : Research Article

Authors

1 MSc. Student, Department of Earth Sciences, University of Tabriz, Tabriz, Iran

2 Associated Professor, Department of Earth Sciences, University of Tabriz, Tabriz

3 Ph.D in Hydrogeology, Department of Earth Sciences, University of Shiraz, Tabriz

4 Assistant Professor, Department of Civil Engineering and Environment, University of Maragheh

5 BSc., Faculty of Engineering, Islamic Azad University of Tabriz, Tabriz

Abstract

The accurate recognition of hydrogeological parameters such as transmissivity, hydraulic conductivity and storage coefficient or specific yield are the most important parameters for predicting the aquifer conditions that are determined at different points of aquifer with great cost. In recent years, artificial intelligence models have been used as alternatives method to adaptive graphical methods to determine the hydrodynamic parameters of aquifers. Therefore, in this study the Sugeno fuzzy logic was used to determine the hydrodynamic parameters of the confined aquifer. First, the accuracy, reliability and generalization ability of the fuzzy model is verified by time-drawdown field data. Then, the results of this model were compared with the results of obtained from the Theis graphical method and artificial neural network. Comparison of the RRMSE of the Sugeno fuzzy model and the artificial neural network for determining the transmissivity and storage coefficient in testing step show that the fuzzy model reduces the error relative to the neural network 9.21% and 11.66%, respectively. Therefore, the results of the Theis graphical method, artificial neural network and fuzzy logic model in the verification step indicates that the Sugeno fuzzy model is able to determine the parameters of confined aquifer. The sugene fuzzy logic model is due to its high ability to contrast with uncertainty data that has more accurate results to the Theis graphical method and artificial neural network.

Keywords

Main Subjects

References

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Iranian journal of Ecohydrology
Volume 5, Issue 4
January 2019
Pages 1079-1089

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History

  • Receive Date: 01 January 2018
  • Revise Date: 28 July 2018
  • Accept Date: 16 July 2018
  • First Publish Date: 22 December 2018
  • Publish Date: 22 December 2018

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