Estimation of irrigation water quality index in a semi-arid environment using data-driven approach

Abstract

The primary objective of this study was to calculate and assess the irrigation water quality index. Furthermore, an effective method for predicting IWQI using artificial neural network (ANN) and multiple linear regression (MLR) models was proposed. The accuracy performance of each model was evaluated at the end of this paper. According to the calculated index based on 49 groundwater samples, the Sidi El Hani aquifer was of good and sufficient quality. Moreover, both the ANN and MLR models performed well in terms of actual and predicted water quality. The ANN model, on the other hand, demonstrated the highest prediction accuracy. The results of this model also revealed that the predicted and computed values were close, with determination coefficients R2, RMSE, and MAE of about 0.95, 1.02, and 0.90, respectively. As a result, the proposed ANN model in this study was consistent and sufficient. These findings will help to guide irrigation water management decisions for the study aquifer in the future. The proposed ANN model can also be used to estimate the irrigation water index of other semi-arid aquifers, but accuracy is dependent on proper training techniques and selection parameters. HIGHLIGHTS Assessment of irrigation water quality (IWQI) index in a semi arid-environment.; Prediction of IWQI using ANN model.; Prediction of IWQI using MLR model.; Effectiveness of a machine learning tool (ANN) in accurately predicitng of IWQI.; Developing an accurate model may be valuable to manage the irrigation water quality.;

Keywords

• artificial neural network
• irrigation water quality index
• multiple linear regression
• salinization
• sidi el hani aquifer