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LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah

Author

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  • Fatin Nadiah Yussof

    (Department of Mathematics, Faculty of Science and Technology, Universiti Teknologi Malaysia, Skudai 81310, Malaysia)

  • Normah Maan

    (Department of Mathematics, Faculty of Science and Technology, Universiti Teknologi Malaysia, Skudai 81310, Malaysia)

  • Mohd Nadzri Md Reba

    (Department of Mathematics, Faculty of Science and Technology, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
    Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Skudai 81310, Malaysia)

Abstract

Harmful algal bloom (HAB) events have alarmed authorities of human health that have caused severe illness and fatalities, death of marine organisms, and massive fish killings. This work aimed to perform the long short-term memory (LSTM) method and convolution neural network (CNN) method to predict the HAB events in the West Coast of Sabah. The results showed that this method could be used to predict satellite time series data in which previous studies only used vector data. This paper also could identify and predict whether there is HAB occurrence in the region. A chlorophyll a concentration (Chl-a; mg/L) variable was used as an HAB indicator, where the data were obtained from MODIS and GEBCO bathymetry. The eight-day dataset interval was from January 2003 to December 2018. The results obtained showed that the LSTM model outperformed the CNN model in terms of accuracy using RMSE and the correlation coefficient r as the statistical criteria.

Suggested Citation

  • Fatin Nadiah Yussof & Normah Maan & Mohd Nadzri Md Reba, 2021. "LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7650-:d:596746
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    References listed on IDEAS

    as
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    3. Sangmok Lee & Donghyun Lee, 2018. "Improved Prediction of Harmful Algal Blooms in Four Major South Korea’s Rivers Using Deep Learning Models," IJERPH, MDPI, vol. 15(7), pages 1-15, June.
    4. Ashutosh Kumar & Tanvir Islam & Yoshihide Sekimoto & Chris Mattmann & Brian Wilson, 2020. "Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-18, March.
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    Cited by:

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