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

Research Article| January 30 2017 A practical low-cost model for prediction of the groundwater quality using artificial neural networks Nima Heidarzadeh Nima Heidarzadeh 1Civil Engineering Group, Technical and Engineering Department, Kharazmi University, Tehran, Iran E-mail: n.heidarzadeh@khu.ac.ir Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2017) 66 (2): 86–95. https://doi.org/10.2166/aqua.2017.035 Article history Received: May 14 2016 Accepted: October 26 2016 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Nima Heidarzadeh; A practical low-cost model for prediction of the groundwater quality using artificial neural networks. Journal of Water Supply: Research and Technology-Aqua 15 March 2017; 66 (2): 86–95. doi: https://doi.org/10.2166/aqua.2017.035 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex 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 © IWA Publishing 2017 You do not currently have access to this content.