article · Water
Water quality assessments in surface irrigation networks require careful monitoring of multiple chemical and physical variables. An evaluation of 110 surface water samples gathered across two consecutive summers examined 21 physicochemical parameters using six water quality indices alongside multivariate statistical models. Geochemical analysis identified calcium and alkalinity as the dominant major ions, influenced by silicate weathering and reverse ion exchange processes. The resulting water quality indices confirmed that the sampled canal networks are suitable for agricultural irrigation, with 82 percent of samples meeting high-grade irrigation standards and the remainder falling into a medium category. Sodium-related indices similarly confirmed that the water remains within safe thresholds for soil and crop health. Furthermore, predictive statistical models, including principal component regression, support vector machine regression, and stepwise multiple linear regression, accurately estimated the quality indices across calibration and validation datasets.
Reliable irrigation water is essential for sustained agricultural productivity and soil conservation. Evaluating canal networks through composite quality indices ensures that water supplies do not degrade arable land through excessive salinity or toxic ion accumulation. Using statistical modelling alongside routine chemical testing simplifies long-term monitoring, offering regional water managers a practical method to safeguard crop health and optimise resource allocation.
This work demonstrates an applied, tested approach to water quality monitoring that combines standard chemical testing with predictive machine learning algorithms. The methodology could be integrated into decision-support software for agricultural authorities, irrigation network managers, and environmental monitoring agencies to automate water classification. Operating at an applied research stage, the techniques rely on standard chemical inputs and could be developed into operational software tools for regional water management.
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Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ > Na+ > Mg2+ > K+ and alkalinity > SO42− > Cl− > NO3− > F−. The trace elements concentrations were reported in the following order: Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd. The surface water belongs to the Ca2+-Mg2+-HCO3− and Ca2+-Mg2+-Cl−-SO42− water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.
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DOI: 10.3390/w12123300
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