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Field Measurements and Machine Learning Algorithms to Monitor Water Quality in Lakes Located in Landscape Parks – A Case Study

cris.virtual.author-orcid0000-0003-0344-585X
cris.virtual.author-orcid0000-0003-3999-7250
cris.virtual.author-orcid0000-0003-4078-9600
cris.virtualsource.author-orcidff7a36ab-d209-401b-822d-12191685f04a
cris.virtualsource.author-orcide32458cb-9a33-41e8-9c1a-a373b123c233
cris.virtualsource.author-orcid9f2b1182-a351-435c-b551-51cc79531363
dc.abstract.enOne of the biggest threats to many lakes is their accelerated eutrophication resulting from anthropogenic pressure, agricultural intensification, and climate change. A very important element of surface water protection in environmentally conserved areas is the proper monitoring of water quality and detection of potential threats by examining the physicochemical properties of water and performing statistical analyses that enable possible exposure of unfavourable trends. The article presents the analyses of the results of measurements made in three lakes located in the Sierakowski Landscape Park. As part of the measurements, water quality indicators i.e., phosphorus, nitrogen, BOD5 and COD, were determined monthly for a year at the inflows and outflows of the studied lakes. The test results of selected water quality indicators were analysed using machine learning algorithms i.e., PCA and k-means. The conducted tests enabled statistical estimation of changes in water quality indicators in the reservoirs and evaluation of their correlation.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Wodnej i Sanitarnej
dc.affiliation.instituteKatedra Budownictwa i Geoinżynierii
dc.contributor.authorWalczak, Natalia
dc.contributor.authorWalczak, Zbigniew
dc.contributor.authorLaks, Ireneusz
dc.date.access2025-05-07
dc.date.accessioned2025-08-14T10:20:20Z
dc.date.available2025-08-14T10:20:20Z
dc.date.copyright2023-12-04
dc.date.issued2024
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if1,5
dc.description.number1
dc.description.points70
dc.description.versionfinal_published
dc.description.volume25
dc.identifier.doi10.12911/22998993/173191
dc.identifier.issn2299-8993
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4235
dc.identifier.weblinkhttps://www.jeeng.net/Field-Measurements-and-Machine-Learning-Algorithms-to-Monitor-Water-Quality-in-Lakes,173191,0,1.html
dc.languageen
dc.relation.ispartofJournal of Ecological Engineering
dc.relation.pages49-64
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enquality of water in lakes
dc.subject.enphosphorus
dc.subject.ennitrogen
dc.subject.enBOD5
dc.subject.enCOD
dc.subject.enPCA
dc.subject.enk-means
dc.subject.enANOVA
dc.subject.enKruskal-Wallis test
dc.titleField Measurements and Machine Learning Algorithms to Monitor Water Quality in Lakes Located in Landscape Parks – A Case Study
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume25