LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah
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 (... Mehr ...
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Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2021 |
Reihe/Periodikum: | International Journal of Environmental Research and Public Health, Vol 18, Iss 7650, p 7650 (2021) |
Verlag/Hrsg.: |
MDPI AG
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Schlagwörter: | chlorophyll a / CNN / LSTM / prediction / satellite data / Medicine / R |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-27281476 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://doi.org/10.3390/ijerph18147650 |