DC FieldValueLanguage
dc.contributor.authorVelimirović, Lazaren_US
dc.contributor.authorJanković, Radmilaen_US
dc.contributor.authorVelimirović, Jelenaen_US
dc.contributor.authorJanjić, Aleksandaren_US
dc.date.accessioned2021-08-23T07:54:57Z-
dc.date.available2021-08-23T07:54:57Z-
dc.date.issued2021-
dc.identifier.issn2073-8994-
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/4638-
dc.description.abstractOne way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.en_US
dc.publisherMDPIen_US
dc.relation.ispartofSymmetryen_US
dc.subjectmachine learning; prediction | classification algorithms | water pump failure | optimizationen_US
dc.titleWastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithmsen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/sym13081518-
dc.identifier.scopus2-s2.0-85113559101-
dc.contributor.affiliationComputer Scienceen_US
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.firstpage1518-
dc.relation.issue8-
dc.relation.volume13-
dc.description.rank~M22-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0001-8737-1928-
crisitem.author.orcid0000-0003-3424-134X-
crisitem.author.orcid0000-0002-3745-3033-
Show simple item record

SCOPUSTM   
Citations

5
checked on Apr 18, 2024

Page view(s)

59
checked on Apr 16, 2024

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.