SOFT COMPUTING APPLICATION TO PREDICT IRRIGATION WATER QUALITY INDEX, CASE STUDY OF OUED HAMMAM NORTH-EAST ALGERIA

Abstract

Water quality for irrigation purposes has yet to be considered seriously in many places around the globe. The present case study at the Oued-Hammam watershed in northeast Algeria aims to investigate using a soft computing technique, namely an artificial neural network, to predict irrigation water quality indicators of Sodium absorption ratio (SAR) and Electrical Conductivity (EC). Fourteen water quality parameters were collected at Zit Emba reservoir from 2010 to 2022. Among them, four parameters, namely Sodium (Na), Calcium (Ca), Magnesium (Mg), and Bicarbonate (HCO₃^-), were used as inputs and SAR as an output. Also, five parameters of Total hardness (TH), Total dissolved solids (TDS), Calcium (Ca), Bicarbonate (HCO₃^-) and Sodium (Na) were used as inputs and EC as an output; the Pearson correlation matrix was used to select the input parameters with respect with the output parameter. The back-propagation neural networks learning algorithm was used to model the SAR and EC's irrigation water quality index (IWQI). The models' performances were evaluated using statistical criteria of correlation coefficient (R) and root mean square error (RMSE). Back propagation neural network learning algorithm maximum correlation coefficient for SAR and EC were 0.98077 and 0.97762, respectively, also with a minimum RMSE of 0.037 for SAR and 101.8 for EC. Thus, the current study suggests that artificial neural network (ANN) models are the most effective tools for predicting water quality parameters. Their outcome can be used effectively in managing and controlling water pollution around the watershed.