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Developing a novel water quality prediction model for a South African aquaculture farm

  • Abagold Limited
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Article number

1782

Pages from-to (Number of pages)

Pages 1782

Journal (Volume, Issue Number)

Water (Switzerland) (Volume 13, Issue 13)

Publication milestones

  • Accepted/In press - 24/06/2021
  • Published - 28/06/2021

Publication status

Published - 28/06/2021

ISSN

2073-4441

External Publication IDs

  • handle.net: 10547/625098
  • Scopus: 85109548813

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