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.

中文翻译：

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灌溉水质指数和多元统计技术在埃及北尼罗河三角洲地表水质评估中的应用

在可持续发展条件下，灌溉系统的水质是一个复杂的问题，涉及多个地表水管理参数的综合影响。因此，这项工作旨在加强地表水质评估和地球化学控制机制，并使用由多元建模技术支持的六个水质指数 (WQI)，如主成分回归 (PCR)，评估灌溉地表水网络的验证，支持向量机回归 (SVMR) 和逐步多元线性回归 (SMLR)。本研究共收集了 2018 年和 2019 年夏季地表水渠道网络中的 110 个地表水样本，并使用标准分析技术测量了 21 个物理和化学参数。理化性质表明主要离子浓度按以下顺序报告：Ca2+ > Na+ > Mg2+ > K+ 和碱度 > SO42− > Cl− > NO3− > F−。微量元素浓度按以下顺序报告：Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd。地表水属于Ca2+-Mg2+-HCO3-和Ca2+-Mg2+-Cl--SO42-水类型，在硅酸盐风化和逆离子交换过程的压力下。两年间 WQI 值的计算表明，82% 的样本代表高等级，其余 18% 构成灌溉用水质量 (IWQ) 值的中等等级，而两年的钠百分比 (Na%) 值表明，96% 的样本属于健康等级，4% 属于允许灌溉等级。此外，钠吸收比 (SAR)、渗透指数 (PI)、凯利指数 (KI) 和残留碳酸钠 (RSC) 值显示所有地表水样本均适合灌溉使用。PCR 和 SVMR 表明准确和稳健的模型可以预测校准 (Cal.) 和验证 (Val.) 的两个数据集中的六个 WQI，R2 值从 0.48 到 0.99 不等。SMLR 提供的 6 个 WQI 估计得很好，R2 值介于 0.66 到 0.99 之间。总之，WQI 和多变量统计分析对于评估地表水质量是有效和适用的。聚合酶链反应，