Determination of salt concentration in water using decision trees and electromagnetic waves. (16th May 2022)

• Record Type: Journal Article
• Authors: Efeoglu, Ebru; Tuna, Gurkan
• Is Part Of: Journal of water and health. Volume 20:Number 5(2022)
• Page Start: 803
• Page End: 815
• Publication Date: 2022-05-16
• Subjects: accuracy -- decision trees -- electromagnetic waves -- Hoeffding tree -- S parameters -- salt concentration; Water quality management -- Periodicals; Water -- Pollution -- Environmental aspects -- Periodicals; Environmental health -- Periodicals; Water quality -- Health aspects -- Periodicals; Water -- Health aspects -- Periodicals; Water -- Pollution -- Health aspects -- Periodicals; Public Health; Water Pollution -- prevention & control; Quality Control; Water Microbiology; Water Supply -- standards; Health & Medicine (General); Hydrology; Environmental health; Water -- Health aspects; Water -- Pollution -- Environmental aspects; Water -- Pollution -- Health aspects; Water quality -- Health aspects; Water quality management; Water; Gezondheid; Periodical; Periodicals; 363.61
• DOI: 10.2166/wh.2022.302
• Languages: English
• ISSNs: 1477-8920

Abstract

Abstract: 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.