Temporal fusion transformer-based prediction in aquaponics

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dc.authorid0000-0002-7318-4926
dc.contributor.authorMetin, Ahmet
dc.contributor.authorKasif, Ahmet
dc.contributor.authorCatal, Cagatay
dc.date.accessioned2026-02-12T21:04:58Z
dc.date.available2026-02-12T21:04:58Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractAquaponics offers a soilless farming ecosystem by merging modern hydroponics with aquaculture. The fish food is provided to the aquaculture, and the ammonia generated by the fish is converted to nitrate using specialized bacteria, which is an essential resource for vegetation. Fluctuations in the ammonia levels affect the generated nitrate levels and influence farm yields. The sensor-based autonomous control of aquaponics can offer a highly rewarding solution, which can enable much more efficient ecosystems. Also, manual control of the whole aquaponics operation is prone to human error. Artificial Intelligence-powered Internet of Things solutions can reduce human intervention to a certain extent, realizing more scalable environments to handle the food production problem. In this research, an attention-based Temporal Fusion Transformers deep learning model was proposed and validated to forecast nitrate levels in an aquaponics environment. An aquaponics dataset with temporal features and a high number of input lines has been employed for validation and extensive analysis. Experimental results demonstrate significant improvements of the proposed model over baseline models in terms of MAE, MSE, and Explained Variance metrics considering one-hour sequences. Utilizing the proposed solution can help enhance the automation of aquaponics environments.
dc.description.sponsorshipQatar National Library
dc.description.sponsorshipOpen Access funding provided by the Qatar National Library.
dc.identifier.doi10.1007/s11227-023-05389-8
dc.identifier.endpage19958
dc.identifier.issn0920-8542
dc.identifier.issn1573-0484
dc.identifier.issue17
dc.identifier.scopus2-s2.0-85161329603
dc.identifier.scopusqualityQ1
dc.identifier.startpage19934
dc.identifier.urihttps://doi.org/10.1007/s11227-023-05389-8
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6745
dc.identifier.volume79
dc.identifier.wosWOS:001005089700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Supercomputing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260212
dc.subjectTime series forecasting
dc.subjectAquaponics
dc.subjectTransformers
dc.subjectAnomaly detection
dc.subjectDeep learning
dc.titleTemporal fusion transformer-based prediction in aquaponics
dc.typeArticle

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