Publication:
Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface

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dc.citedby3
dc.contributor.authorRizal N.N.M.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorid57654708600en_US
dc.contributor.authorid56239664100en_US
dc.date.accessioned2024-10-14T03:19:36Z
dc.date.available2024-10-14T03:19:36Z
dc.date.issued2023
dc.description.abstractEfficiency of a system in a sewage treatment plant (STP) is significant in providing high quality of treated water to be discharged for the usage of surrounding neighborhood. However, the problems in measuring and monitoring the water quality in the treated wastewater or effluent water in real time has made it difficult to maintain the efficiency and preserve the energy of the STP. Therefore, this study purposes a graphical user interface (GUI) that has been embedded with a machine learning model to predict effluent parameters in real time. In this study, artificial neural network (ANN) and support vector machine were developed to predict biochemical oxygen demand (BODeff) using several effluent variables. Both models were evaluated using correlation coefficient (R), root mean square error (RMSE), determination of coefficient (R2), and mean square error (MSE). Based on the results, ANN model has outperformed SVM model by achieving the value of MSE and RMSE < 0.02 while R and R2 near to the value of 1.00. Therefore, the ANN model has been inserted into the GUI as ANN model is the most optimum model to predict BODeff in real time. The GUI-based app also performed well and able to predict the parameter with great accuracy. � 2022, University of Tehran.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4
dc.identifier.doi10.1007/s41742-022-00493-8
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85142227241
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85142227241&doi=10.1007%2fs41742-022-00493-8&partnerID=40&md5=95646dbe89f33ebab6e9078643a4af64
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34413
dc.identifier.volume17
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Environmental Research
dc.subjectDomestic wastewater
dc.subjectEfficiency
dc.subjectEnergy
dc.subjectGUI
dc.subjectIoT
dc.subjectSewage treatment plant
dc.subjectSupervised machine learning
dc.subjectartificial neural network
dc.subjectbiochemical oxygen demand
dc.subjectdomestic waste
dc.subjecteffluent
dc.subjectenergy efficiency
dc.subjectforecasting method
dc.subjectmachine learning
dc.subjectreal time
dc.subjectsewage treatment
dc.subjectwater quality
dc.titleForecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interfaceen_US
dc.typeArticleen_US
dspace.entity.typePublication
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