New strategy based on Hammerstein-Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia

The agricultural sector faces challenges in managing water resources efficiently, particularly in arid regions dealing with water scarcity. To overcome water stress, treated wastewater (TWW) is increasingly utilized for irrigation purpose to conserve available freshwater resources. There are several critical aspects affecting the suitability of TWW for irrigation including salinity which can have detrimental effects on crop yield and soil health. Therefore, this study aimed to develop a novel approach for TWW salinity prediction using artificial intelligent (AI) ensembled machine learning approach. In this regard, several water quality parameters of the TWW samples were collected through field investigation from the irrigation zones in Al-Hassa, Saudi Arabia, which were later assessed in the lab. The assessment involved measuring Temperature (T), pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Salinity, through an Internet of Things (IoT) based system integrated with a real-time monitoring and a multiprobe device. Based on the descriptive statistics of the data and correlation obtained through the Pearson matrix, the models were formed for predicting salinity by using the Hammerstein-Wiener Model (HWM) and Support Vector Regression (SVR). The models' performance was evaluated using several statistical indices including correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE). The results revealed that the HWM-M3 model with its superior predictive capabilities achieved the best performance, with R2 values of 82% and 77% in both training and testing stages. This study demonstrates the effectiveness of AI-ensembled machine learning approach for accurate TWW salinity prediction, promoting the safe and efficient utilization of TWW for irrigation in water-stressed regions. The findings contribute to a growing body of research exploring AI applications for sustainable water management.