Artificial Neural Network (ANN) Model for Ground Water Quality Simulation (Case study: Kashan aquifer)

Mirzavand, Mohammad; Ghasemieh, Hoda; Akbari, Mahmud; Sadatinejad, Seyed Javad

doi:10.22059/jrwm.2015.53892

Document Type : Research Paper

Authors

¹ M.Sc. Former Student of Watershed Management, College of Natural Resources and Geoscience, University of Kashan

² Assistant professor, College of Natural Resources and Geoscience, University of Kashan

³ 4 Assistant professor, College of civil engineering, University of Kashan

⁴ associate professor, College of new sciences and technologies, University of Tehran

https://doi.org/10.22059/jrwm.2015.53892

Abstract

Kashan aquifer is adjacent to Salt Lake. Because of this adjacency, the saline water of the lake has moved to the aquifer. In this study groundwater quality of the aquifer was simulated using Artificial Neural Network (ANN) model. For this purpose, the dominant ion of water was first determined by Piper diagram. Results showed that the sodium chloride is the dominant ion of water and so it was selected as the target variable to be simulated So the output variable of the ANN model was the concentration of sodium chloride in current year while the input variables were the water table of groundwater, yearly rainfall and the concentration of sodium chloride in previous year. Result showed that Multilayer Perceptron ANN model has better result in predict of chlorine concentration compared to Radial Basis ANN model. The sensitivity analysis showed that concentration of chloride in previous year and water table of groundwater are the most important variables in the ANN model respectively.

Keywords

References

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Journal of Range and Watershed Managment

Artificial Neural Network (ANN) Model for Ground Water Quality Simulation (Case study: Kashan aquifer)

References

References

Volume 68, Issue 1 - Serial Number 1
June 2015
Pages 159-171

Artificial Neural Network (ANN) Model for Ground Water Quality Simulation (Case study: Kashan aquifer)

References

References

Volume 68, Issue 1 - Serial Number 1June 2015Pages 159-171

Volume 68, Issue 1 - Serial Number 1
June 2015
Pages 159-171