Artificial Neural Network Assessment of Groundwater Quality for Agricultural Use in Babylon City: An Evaluation of Salinity and Ionic Composition

In recent years, the decline in the level of Iraq's rivers necessitated groundwater use for irrigation.A data set collected from 111 wells in the Nile region of Babylon City was analyzed in this research to assess whether or not the groundwater is suitable for irrigation.The studied parameters included electrical conductivity, pH, sulfate, chloride, magnesium, calcium, potassium, sodium, nitrate, bicarbonate, and Total Dissolved Solids (TDS).The quantification of water salinity was achieved through statistical analysis of the examined data, as well as the computation of the sodium adsorption ratio (SAR), magnesium adsorption ratio (MAR), and percentage of dissolved sodium (Na%).Statistical packages for the social sciences (SPSS) software were utilized to generate mathematical models that forecast the quality of groundwater suitable for irrigation, employing artificial intelligence in the form of neural networks.The obtained results indicated that a large proportion of samples fall into the undesirable category, and therefore the groundwater in this region is unsuitable for irrigation.In comparison, it was found that the predicted models have high accuracy and coefficients of determination up to 0.984 for TDS, 0.952 for SAR, 0.918 for Na%, and 0.933 for MAR, with a relative error of no more than 0.056.