Aquaculture 4.0: Hybrid Neural Network Multivariate Water Quality Parameters Forecasting Model

Article


Eze, E., Kirby, S., Attridge, J. and Ajmal, T. 2024. Aquaculture 4.0: Hybrid Neural Network Multivariate Water Quality Parameters Forecasting Model. Scientific Reports. 13 (Art. 16129). https://doi.org/10.1038/s41598-023-41602-7
AuthorsEze, E., Kirby, S., Attridge, J. and Ajmal, T.
Abstract

This study examined the efficiency of hybrid deep neural network and multivariate water quality forecasting model in aquaculture ecosystem. Accurate forecasting of critical water quality parameters can allow for timely identification of possible problem areas and enable decision-makers to take pre-emptive remedial actions that can significantly improve water quality management in aquaculture industry. A novel hybrid deep learning neural network multivariate water quality parameters forecasting model is developed with the aid of ensemble empirical mode decomposition (EEMD) method, deep learning long-short term memory (LSTM) neural network (NN), and multivariate linear regression (MLR) method. The presented water quality forecasting model (shortened as EEMD-MLR-LSTM NN model) is developed using multivariate time-series water quality sensor data collected from Loch Duart company, a Salmon offshore aquaculture farm based around Scourie, northwest Scotland. The performance of the novel hybrid water quality forecasting model is validated by comparing the forecast result with measured water quality parameters data and the real Phytoplankton data count from the aquaculture farm. The forecast accuracy of the results suggests that the novel hybrid water quality forecasting model can be used as a valuable support tool for water quality management in aquaculture industries.

KeywordsWater quality; Deep learning LSTM; Aquaculture; Forecasting; Chlorophyll-a
JournalScientific Reports
Journal citation13 (Art. 16129)
ISSN2045-2322
Year2024
PublisherSpringer Nature
Accepted author manuscript
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Repository staff only
Publisher's version
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Anyone
Digital Object Identifier (DOI)https://doi.org/10.1038/s41598-023-41602-7
Publication dates
Online26 Sep 2023
Publication process dates
Accepted29 Aug 2023
Deposited03 Jun 2024
FunderInnovate UK
Biotechnology and Biological Sciences Research Council (BBSRC)
Copyright holder© 2024, The Authors
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License: CC BY 4.0
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