Optimizing Water Quality Assessment for Agriculture Using Multivariate Methods and Decision‐Making Models

ABSTRACT The growing demand for water in agriculture requires a thorough assessment of water quality to ensure sustainable irrigation practices. This study integrates a novel approach, the method of removal effects of criteria (MEREC), with principal component analysis (PCA), which delves into the intricate interplay of key water quality parameters within the Karun River Basin, a vital agricultural region in Iran. The key water quality parameters include total dissolved solids (TDS), electrical conductivity (EC), the sodium adsorption ratio (SAR), the permeability index (PI), total hardness (TH), the sodium percentage (SP), the magnesium adsorption ratio (MAR) and potential salinity (PS). The results revealed strong correlations between EC, TDS and SAR. PCA revealed that EC, SAR and PS were the primary contributors to the variations in water quality, explaining more than 89% of the total variance. The MEREC method revealed that PS was the most significant factor, followed by EC and SAR. The calculated water quality index (WQI) indicated that 74% of the water samples fell within the low‐restriction category, whereas 26% exhibited moderate restrictions. These findings suggest that although the water of Karun River Basin is generally suitable for irrigation, the salinity and sodium content may pose challenges to agricultural productivity.