Water Quality Evaluation and Prediction Using Irrigation Indices, Artificial Neural Networks, and Partial Least Square Regression Models for the Nile River, Egypt

Water quality is identically important as quantity in terms of meeting basic human needs. Therefore, evaluating the surface-water quality and the associated hydrochemical characteristics is essential for managing water resources in arid and semi-arid environments. Therefore, the present research was conducted to evaluate and predict water quality for agricultural purposes across the Nile River, Egypt. For that, several irrigation water quality indices (IWQIs) were used, along with an artificial neural network (ANN), partial least square regression (PLSR) models, and geographic information system (GIS) tools. The physicochemical parameters, such as T &deg:C, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl&minus:, SO42&minus:, HCO3&minus:, CO32&minus:, and NO3&minus:, were measured at 51 surface-water locations. As a result, the ions contents were the following: Ca2+ &gt: Na+ &gt: Mg2+ &gt: K+ and HCO3&minus: &gt: Cl&minus: &gt: SO42&minus: &gt: NO3&minus: &gt: CO32&minus:, reflecting Ca-HCO3 and mixed Ca-Mg-Cl-SO4 water types. The irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), permeability index (PI), and magnesium hazard (MH) had mean values of 92.30, 1.01, 35.85, 31.75, 72.30, and 43.95, respectively. For instance, the IWQI readings revealed that approximately 98% of the samples were inside the no restriction category, while approximately 2% of the samples fell within the low restriction area for irrigation. The ANN-IWQI-6 model&rsquo:s six indices, with R2 values of 0.999 for calibration (Cal.) and 0.945 for validation (Val.) datasets, are crucial for predicting IWQI. The rest of the models behaved admirably in terms of predicting SAR, Na%, SSP, PI, and MR with R2 values for the Cal. and validation Val. of 0.999. The findings revealed that ANN and PLSR models are effective methods for predicting irrigation water quality to assist decision plans. To summarize, integrating physicochemical features, WQIs, ANN, PLSR, models, and GIS tools to evaluate surface-water suitability for irrigation offers a complete image of water quality for sustainable development.