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dc.contributor.authorChandramenon, Praveen
dc.contributor.authorGascoyne, Andrew
dc.contributor.authorTchuenbou-Magaia, Fideline Laure
dc.date.accessioned2024-06-07T09:47:22Z
dc.date.available2024-06-07T09:47:22Z
dc.date.issued2024-05-06
dc.identifier.citationChandramenon P, Gascoyne A and Tchuenbou-Magaia F ( 2024) IoT and machine learning approach for the determination of optimal freshwater replenishment rate in aquaponics system. Frontiers in Sustainable Resource Management, 3: 1363914.en
dc.identifier.issn2813-3005en
dc.identifier.doi10.3389/fsrma.2024.1363914en
dc.identifier.urihttp://hdl.handle.net/2436/625543
dc.description© 2024 The Authors. Published by Frontiers Media. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3389/fsrma.2024.1363914en
dc.description.abstractConventional aquaponics conserve water used in aquaponics whereas the literature suggests a certain level of freshwater replenishment or freshwater exchange for good water quality, fish and plant wellbeing, and the overall productivity of the system. This paper deals with the determination of an optimal freshwater replenishment rate for a standard aquaponics system. IoT devices and sensors were used for this project data collection. This paper used linear regressions and ensemble methods to determine the optimal rate of periodic water replenishment to maintain the water quality parameters that determine the yield and productivity of aquaponics systems. Cubic spline and Lagrange interpolation were applied to raw and simulated data. Results were evaluated and compared using statistical error estimation approaches. The best model amongst the investigated machine learning models was gradient boost with an optimal replenishment rate of 19L per week and a water quality of 4.86 for an aquaponic tank of 100 L capacity. The error estimations were a Mean Squared Error of 0.0224, Mean Absolute Error of 0.1137, Root Mean Squared Error of 0.1499, and R2 of 0.7208. This was within 1% of the value obtained from raw and interpolated data using a polynomial regression. These findings suggest that the water quality of an aquaponics system can be maintained at the desired optimal level with a weekly 19% water replenishment, thereby contributing to the improvement of productivity and resource efficiency.en
dc.description.sponsorshipThis project has been partially internally funded by the School of Engineering, Computing, and Mathematical Sciences at the University of Wolverhampton and supported by the EU Horizon 2020 MSCA RISE Project ReACTIVE Too, Grant Agreement No 871163.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherFrontiers Mediaen
dc.relation.urlhttps://doi.org/10.3389/fsrma.2024.1363914en
dc.subjectinternet of thingsen
dc.subjectIoTen
dc.subjectaquaponicsen
dc.subjectwater qualityen
dc.subjectwater replenishmenten
dc.subjectmachine learningen
dc.titleIoT and machine learning approach for the determination of optimal freshwater replenishment rate in aquaponics systemen
dc.typeJournal articleen
dc.identifier.eissn2813-3005
dc.identifier.journalFrontiers in Sustainable Resource Managementen
dc.date.updated2024-06-06T04:14:27Z
dc.identifier.articlenumber1363914
dc.date.accepted2024-04-07
rioxxterms.funderEU Horizon 2020 MSCA RISE Project ReACTIVE Tooen
rioxxterms.identifier.project871163en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2024-06-07en
dc.source.volume3
dc.source.beginpage1
dc.description.versionPublished online
refterms.dateFCD2024-06-07T09:47:15Z
refterms.versionFCDVoR
refterms.dateFOA2024-06-07T09:47:24Z


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