Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 51666, Iran.
Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam.
Environ Monit Assess. 2020 Aug 9;192(9):575. doi: 10.1007/s10661-020-08506-9.
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.
地表水质量控制在水资源管理中起着重要作用。在这方面，需要估计和评估钠离子吸附比（SAR），这是农业生产部门的重要水质参数之一。然而，在所有的国家尺度河流中，化学分析可能无法在更长的时间内进行。因此，在这项研究中，使用了支持向量回归（SVR）模型和不同的核函数、K 最近邻算法以及四种决策树模型，即 Hoeffding 树、随机森林、随机树和 REPTree，以最小的参数估计土耳其阿拉达格河的 SAR 值。在替代方案中，使用相关矩阵和敏感性分析来确定 15 个不同参数中的模型输入。在第一个方案中，将所有 15 个参数都用作模型输入，而在最后一个方案中，仅将钠（Na）参数用作模型输入。然后使用相关系数、纳什-苏特克利夫模型效率系数、均方根误差、平均绝对误差和威尔莫特一致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 模型提供了 SAR 的最佳估计。根据研究结果，第一和最后方案的结果之间没有显著差异，可以考虑仅使用 Na 参数并利用机器学习方法来确定 SAR 值。
