The dissolved oxygen prediction based on the machine learning techniques

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dc.citation.epage488
dc.citation.rankM33
dc.citation.spage485
dc.contributor.authorVoza, Danijela
dc.contributor.authorDehghani, Hesam
dc.contributor.authorVeličković, Milica
dc.date.accessioned2024-01-10T10:56:08Z
dc.date.available2024-01-10T10:56:08Z
dc.date.issued2023
dc.description.abstractOne of the most reliable indicators of surface water quality is dissolved oxygen (DO). In order to take timely reactions in water pollution prevention and reduction, it is very useful to predict the changes in this parameter. In this study, a comparative analysis of the efficiency of different machine learning models in DO prediction was carried out. The aim was to examine which of the selected techniques indicate the best performance in DO prediction - Decision Tree, Random Forest, Gradient Boost Regression, Support Vector Regression, Multi-Layer Perceptron, and K - Nearest Neighbors Regression (KNN). According to the results, it can be concluded the best-fitted model on the created dataset is KNN.
dc.identifier.isbn978-86-6305-140-9
dc.identifier.urihttps://ioc.tfbor.bg.ac.rs/public/2023/Proceedings_IOC_2023.pdf
dc.identifier.urihttps://repozitorijum.tfbor.bg.ac.rs/handle/123456789/5794
dc.language.isoen
dc.publisherUniversity of Belgrade, Technical Faculty in Bor
dc.rights.licenseCC-BY-NC-ND
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceProceedings - 54th International October Conference on Mining and Metallurgy - IOC 2023, 18-21 October 2023, Bor Lake, Serbia, 2023, 485-488
dc.subjectDissolved Oxygen
dc.subjectMachine learning
dc.subjectPrediction
dc.subjectWater quality
dc.subjectTisa River
dc.titleThe dissolved oxygen prediction based on the machine learning techniques
dc.typeconferenceObject
dc.type.versionpublishedVersion

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