The control of surface water quality plays an important role in the management of water resources. In this context, the estimation and assessment of sodium adsorption ratio (SAR) are required which is one of the significant water quality parameters in the agricultural production sector. Chemical analysis might not, however, be feasible for a longer period of time in all the country-scale rivers. Therefore in this study, a support vector regression (SVR) model with different kernel functions; K nearest neighbour algorithm; and four decision-tree models, namely, Hoeffding tree, random forest, random tree, and REPTree, were used to estimate the SAR value with minimal parameters in the Aladag River in Turkey. In alternative scenarios, a correlation matrix and sensitivity analysis were used to ascertain the model inputs from among the 15 distinct parameters. All 15 parameters were utilized as model inputs in the first scenario, and only the sodium (Na) parameter was utilized as the model input in the final scenario. The accuracy of the aforesaid models was then assessed making use of correlation coefficient, Nash-Sutcliffe model efficiency coefficient, root mean square error, mean absolute error, and Willmott index of agreement. The results indicate that the SVR model with the poly kernel function provides the best estimates of SAR among the considered models. According to the findings, there is no considerable difference between the results acquired in the first and last scenarios, and one can determine the SAR value while making use of machine learning approaches taking into account only Na parameter.

中文翻译：

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基于核和决策树模型的河流中钠吸附率估算

地表水水质的控制在水资源管理中起着重要作用。在这种情况下，需要对钠吸附率（SAR）进行估算和评估，这是农业生产部门重要的水质参数之一。但是，在所有国家/地区规模的河流中，化学分析在较长时间内可能都不可行。因此，在本研究中，我们采用了具有不同内核功能的支持向量回归（SVR）模型。K最近邻算法；分别采用Hoeffding树，随机森林，随机树和REPTree这四种决策树模型，以最小的参数估算土耳其阿拉达河的SAR值。在其他情况下，相关矩阵和敏感性分析用于从15个不同的参数中确定模型输入。在第一种情况下，所有15个参数均用作模型输入，而在最终情况下，仅将钠（Na）参数用作模型输入。然后利用相关系数，Nash-Sutcliffe模型效率系数，均方根误差，平均绝对误差和一致性的Willmott指数评估上述模型的准确性。结果表明，在考虑的模型中，具有多核函数的SVR模型可提供SAR的最佳估计。根据调查结果，在第一种情况和最后一种情况下获得的结果之间没有显着差异，