The dissolved oxygen prediction based on the machine learning techniques
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Date
2023
Authors
Voza, Danijela
Dehghani, Hesam
Veličković, Milica
Journal Title
Journal ISSN
Volume Title
Publisher
University of Belgrade, Technical Faculty in Bor
Source
Proceedings - 54th International October Conference on Mining and Metallurgy - IOC 2023, 18-21 October 2023, Bor Lake, Serbia, 2023, 485-488
Volume
Issue
Abstract
One 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.
Description
Keywords
Dissolved Oxygen, Machine learning, Prediction, Water quality, Tisa River
Citation
DOI
Scopus
ISSN
ISBN
978-86-6305-140-9
License
CC-BY-NC-ND