Artificial Neural Network Model for the Prediction of Groundwater Quality

The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from ۱۱۴ wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO۴), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be ۱۴.۹ %, ۳۹.۵ %, ۲۲.۸ %, ۶.۱ %, and ۱۶.۷ %, respectively. Additionally, to anticipate changes in groundwater WQI, IBM® SPSS® Statistics ۱۹ software (SPSS) was used to develop an artificial neural network model (ANNM). With the application of this ANNM model, the results obtained illustrated high prediction efficiency, as the sum of squares error functions (for training and testing samples) and coefficient of determination (R۲), were found to be (۰.۰۳۸ and ۰.۰۰۵) and ۰.۹۷۳, respectively. However, the parameters pH and Cl influenced model prediction significantly, thereby becoming crucial factors in the anticipation carried out by using ANNM model.

Keywords:

Assessment and Prediction , Groundwater Quality , Human Health , Water Quality Index , Artificial Neural Network Model