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

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.