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Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm

1
Institute for Research in Applicable Computing (IRAC), School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK
2
Research and Development Department, Abagold Limited, Hermanus 7200, South Africa
*
Author to whom correspondence should be addressed.
Academic Editor: Bahram Gharabaghi
Water 2021, 13(13), 1782; https://doi.org/10.3390/w13131782
Received: 18 May 2021 / Revised: 21 June 2021 / Accepted: 24 June 2021 / Published: 28 June 2021
(This article belongs to the Section Water Quality and Contamination)
Providing 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. View Full-Text
Keywords: water quality prediction; deep learning; long-short term memory; ensemble empirical mode decomposition; neural network; aquaculture; data filling; correlation analysis water quality prediction; deep learning; long-short term memory; ensemble empirical mode decomposition; neural network; aquaculture; data filling; correlation analysis
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MDPI and ACS Style

Eze, E.; Halse, S.; Ajmal, T. Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm. Water 2021, 13, 1782. https://doi.org/10.3390/w13131782

AMA Style

Eze E, Halse S, Ajmal T. Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm. Water. 2021; 13(13):1782. https://doi.org/10.3390/w13131782

Chicago/Turabian Style

Eze, Elias, Sarah Halse, and Tahmina Ajmal. 2021. "Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm" Water 13, no. 13: 1782. https://doi.org/10.3390/w13131782

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