Journal of Water & Health ( IF 2.3 ) Pub Date : 2022-05-01 , DOI: 10.2166/wh.2022.302 Ebru Efeoglu 1 , Gurkan Tuna 2
Salt water adversely affects human health and plant growth. In parallel with the increasing interest in non-contact determination of salt concentration in water, a novel approach is proposed in this study. In the proposed approach, S parameter measurements, which show the scattering properties of electromagnetic waves, are used. First, the relationship between salt concentration in water and permittivity values, a distinguishing feature for liquids, is shown. Then, based on the derived correlations from a set of S parameter measurements, it is shown that the salt concentration in water can be predicted. Finally, after exactly determining the relations of permittivity, salt concentration and S parameter, a system that allows non-contact determination of salt concentration is proposed. Since the proposed system makes its prediction using a classifier, decision tree algorithms are employed for this purpose. In order to evaluate the appropriateness and success of the algorithms, a set of classification experiments were held using various water samples with different levels of salt concentration. The results of the classification experiments show that the Hoeffding tree algorithm achieved the best results and is the most suitable decision tree algorithm for determining the salt concentration of liquids. For this reason, the proposed non-contact approach can be used to determine the salt concentration in water reliably and quickly if its hardware and software components can be embedded into a prototype system.
HIGHLIGHTS
Showing the relationship between salt concentration in water and permittivity values.
Predicting salt concentration.
Proposing a system that allows non-contact determination of salt concentration.
中文翻译:
使用决策树和电磁波测定水中的盐浓度
盐水对人类健康和植物生长产生不利影响。随着人们对非接触式测定水中盐浓度的兴趣日益增加,本研究提出了一种新方法。在所提出的方法中,使用了显示电磁波散射特性的S参数测量。首先,显示了水中盐浓度与介电常数值之间的关系,这是液体的一个显着特征。然后,基于从一组S参数测量中导出的相关性,表明可以预测水中的盐浓度。最后,在准确确定介电常数、盐浓度和S的关系后参数,提出了一种允许非接触式测定盐浓度的系统。由于所提出的系统使用分类器进行预测,因此为此目的采用了决策树算法。为了评估算法的适当性和成功性,使用具有不同盐浓度水平的各种水样进行了一组分类实验。分类实验结果表明,Hoeffding树算法取得了最好的效果,是最适合确定液体盐浓度的决策树算法。出于这个原因,如果其硬件和软件组件可以嵌入到原型系统中,所提出的非接触式方法可用于可靠、快速地确定水中的盐浓度。
强调
显示水中盐浓度与介电常数值之间的关系。
预测盐浓度。
提出一种允许非接触式测定盐浓度的系统。