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  • Volume 66, Issue 2

A practical low-cost model for prediction of the groundwater quality using artificial neural networks

Nima Heidarzadeh
Published March 2017, 66 (2) 86-95; DOI: 10.2166/aqua.2017.035
Nima Heidarzadeh
Civil Engineering Group, Technical and Engineering Department, Kharazmi University, Tehran, Iran E-mail: n.heidarzadeh@khu.ac.ir
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Abstract

The quality monitoring of water wells is always a costly and time-consuming process. To avoid the unnecessary cost and time of future sampling, applying some powerful and well known prediction models can be a suitable alternative. In this research, the groundwater quality of Amol-Babol aquifer was predicted using the artificial neural network (ANN) model with a data set from 1987 to 2010. Sodium (Na) was considered as the response variable in the ANN model due to its high concentration for irrigation. Also, to select the studied wells in the neural network, a geographic information system (GIS)-based zoning of Na was conducted for 20 years. Afterwards, the sensitive area was detected. Based on pre-modeling, the three properties of pH, electrical conductivity and total hardness were the best input variables. The results indicated that the Na concentration in three wells can be estimated by training six monitoring wells with a high accuracy. The best network is a two-layer network of the Logsig-Tansig transfer functions with four and three neurons in the first and second layers, respectively. In the best model, the coefficients of determination (R2) were 0.99 and 0.98 for the training and the validation periods, respectively, with a root mean square error of 0.08.

  • artificial neural networks
  • ANN
  • feed-forward networks
  • groundwater
  • sodium
  • First received 14 May 2016.
  • Accepted in revised form 26 October 2016.
  • © IWA Publishing 2017
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Journal of Water Supply: Research and Technology - Aqua: 67 (2)
  Volume 66, Issue 2

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A practical low-cost model for prediction of the groundwater quality using artificial neural networks
Nima Heidarzadeh
Journal of Water Supply: Research and Technology - Aqua Mar 2017, 66 (2) 86-95; DOI: 10.2166/aqua.2017.035
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A practical low-cost model for prediction of the groundwater quality using artificial neural networks
Nima Heidarzadeh
Journal of Water Supply: Research and Technology - Aqua Mar 2017, 66 (2) 86-95; DOI: 10.2166/aqua.2017.035

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Keywords

artificial neural networks
ANN
feed-forward networks
groundwater
sodium
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