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dc.contributor.authorEze, Elias Chinedum
dc.contributor.authorHalse, Sarah
dc.contributor.authorAjmal, Tahmina
dc.date.accessioned2021-09-06T10:15:12Z
dc.date.available2021-06-28T00:00:00Z
dc.date.available2021-09-06T10:15:12Z
dc.date.issued2021-06-28
dc.identifier.citationEze E, Halse S, Ajmal T (2021) 'Developing a novel water quality prediction model for a South African aquaculture farm', Water, 13 (13), 1782.en_US
dc.identifier.issn2073-4441
dc.identifier.doi10.3390/w13131782
dc.identifier.urihttp://hdl.handle.net/10547/625098
dc.description.abstractProviding an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.en_US
dc.description.sponsorshipInnovate UK/BBSRC (ref: 86204028, BB/S020896/1)en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.urlhttps://www.mdpi.com/2073-4441/13/13/1782/htmen_US
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectaquaculture water qualityen_US
dc.subjectEEMDen_US
dc.subjectforecastingen_US
dc.subjectLSTMen_US
dc.subjectaquacultureen_US
dc.subjectwater qualityen_US
dc.subjectwater contaminationen_US
dc.subjectWater Quality Indexen_US
dc.subjectsurface wateren_US
dc.subjectSubject Categories::H122 Water Quality Controlen_US
dc.titleDeveloping a novel water quality prediction model for a South African aquaculture farmen_US
dc.typeArticleen_US
dc.identifier.eissn2073-4441
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.contributor.departmentAbagold Limiteden_US
dc.identifier.journalWateren_US
dc.date.updated2021-09-06T10:04:56Z
dc.description.noteAll the authors have agreed to submit this journal article for a deposit in the repository. [researcher] gold OA


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