Determination of salt concentration in water using decision trees and electromagnetic waves

Efeoglu, Ebru; Tuna, Gürkan

doi:10.2166/wh.2022.302

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…This observed decline in model performance after conducting 10-fold cross-validation is consistent with the findings of a separate study that investigated the prediction of salt concentration in water samples. The study identified a decision forest algorithm as the most effective classifier in that context [ 59 ].…”

Section: Resultsmentioning

confidence: 99%

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Tselemponis,

Stefanis,

Giorgi

et al. 2023

IJERPH

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In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009–2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

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Section: Resultsmentioning

confidence: 99%

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Tselemponis,

Stefanis,

Giorgi

et al. 2023

IJERPH

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“…Here the author's state that the averaged BOD and COD are related and can lead to a far more precise and effective study. The authors [16] describe an optimized random forest method for the categorization of arsenic (As) levels in groundwater samples taken from the Indo-Gangetic area. The gradient boost approach is recommended by the authors of [17] for BOD prediction.…”

Section: Introductionmentioning

confidence: 99%

IoT-Enabled Embedded Virtual Sensor for Energy-Efficient Scalable Online Water Quality Monitoring System

Aleefia .A. Khurshid

2024

jes

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Pollutants introduced into water sources by industries and individuals have made it difficult to obtain enough clean drinking water in the modern world. This calls for a mechanism that periodically assesses the water's quality. There are portable instruments available to measure the factors impacting water quality. But the user needs solutions that are inexpensive, automated, reliable, and responsive. For monitoring of large water bodies, a low-cost, scalable, edge processing system in real-time is created to satisfy the current needs. It is designed using a Raspberry Pi, a display unit, and standard sensors for variables including temperature, ORP, pH, conductivity, and DO. Edge intelligence along with tiny ML is used to deploy the virtual sensor for estimation of the degree of pollution in water which otherwise requires a costly and tedious process. With an edge reaction time of 2.70ns and a notable Nash-Sutcliffe efficiency coefficient of 0.99 for BOD and COD estimation in water, this model is created utilizing the randomized tree classification. With the use of a power-efficient algorithm, the 8.4w system energy consumption of the created prototype allows it to operate on AC and solar power and can be effective in difficult climatic conditions.

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“…This provides the possibility of generating and transferring energy between biological molecules and this realizes the phenomenon of life and causes the occurrence of biological anomalies of water in living organisms (features of electrical conductivity, freezing, etc.). However, the mechanisms for realizing this important role of water as an integral component of the magnetoelectrochemical basis of the phenomenon of life at the molecular level in living biological systems have long remained undiscovered and are still being studied [3,[5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Modern biophysical view of electromagnetic processes of the phenomenon of life of living biological systems as a promising basis for the development of complex medicine: the role of water

Невойт¹,

Bumblytė²,

Potyazhenko³

et al. 2022

J., complex., health sci.

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The results of a theoretical study of the role of water in electromagnetic generation and in the process of realizing the phenomenon of life are presented in the article. This article is a continuation of the theoretical study of the team of authors, which was published in the Journal of Complexity in Health Sciences, Vol. 5, Issue 1, 2022, p. 22-34. The aim of the theoretical study was to generalize the available scientific physical and biological knowledge of modern science about the role of water in the electromagnetic processes of the phenomenon of life at the cellular level in order to deepen the fundamental knowledge of Complex Medicine. This study is a fragment of research work on “Development of algorithms and technologies for implementing a Healthy Lifestyle in patients with Noncommunicable Diseases based on the study of functional status” (state registration number 0121U108237: UDC 613 616-056-06: 616.1/9-03). General scientific methods and theoretical methods were used in this theoretical study. The main conclusions of the theoretical study were made as follows: 1) Тhe “30/11 helix” is the main most typical fragmentary element of water self-organization into fractal structures and it forms energy-intensive fractal crystalline filamentous water structures in living biological systems, supported by energy constantly coming from biopolymers, and forming large constantly growing hierarchical structures that unite all the water of a living organism in a dynamic balance of opposite processes of destruction and growth of its energy-intensive crystalline structures; 2) Water has a key role in the process of transmission and perception of the coherent energy of a soliton through self-organizing fractal energy-intensive paramagnetic crystalline structures in living biological systems; 3) The phenomenon of biological life at the micro level is characterized by the presence of a state of water binding/organization into energy-intensive crystalline structures due to the constant supply of coherent energy from biopolymers and stops when this energy flow disappears, which corresponds to the onset of the phenomenon of biological death and is manifested by the degradation of energy-intensive fractal crystalline self-organization of water to an unbound water state. solution devoid of so-called biological anomalies.

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Determination of salt concentration in water using decision trees and electromagnetic waves

Cited by 4 publications

References 18 publications

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

IoT-Enabled Embedded Virtual Sensor for Energy-Efficient Scalable Online Water Quality Monitoring System

Modern biophysical view of electromagnetic processes of the phenomenon of life of living biological systems as a promising basis for the development of complex medicine: the role of water

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