Early Prediction of Margalefidinium polykrikoides Bloom Using a LSTM Neural Network Model in the South Sea of Korea SCIE SCOPUS

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Title
Early Prediction of Margalefidinium polykrikoides Bloom Using a LSTM Neural Network Model in the South Sea of Korea
Author(s)
Shin, Jisun; Kim, Soo Mee; Son, Young Baek; Kim, Keunyong; Ryu, Joo-Hyung
KIOST Author(s)
Shin, Jisun(신지선)Kim, Soo Mee(김수미)Son, Young Baek(손영백)Kim, Keunyong(김근용)Ryu, Joo Hyung(유주형)
Publication Year
2019-09
Abstract
Harmful algal blooms (HABs) have been occurring within the South Sea of Korea (SSK) for decades, causing significant ecological impacts and economic problems for many fish farms concentrated in coastal areas. The occurrence of HABs is related to various factors, including meteorological and physical factors, making it difficult to predict the timing of their occurrence. However, it is essential to make a preliminary forecast of HAB occurrence through analysis of such factors to minimize the damage. In this study, a deep neural network model of long short-term memory (LSTM) that can predict the occurrence time of Margalefidinium polykrikoides blooms is presented and evaluated. Satellite data were used to extract sea surface temperature (SST) and photosynthetically available radiation (PAR), which are environmental factors known to be related to HABs occurrence. M. polykrikoides blooms that have occurred in the past 21 years (1998-2018) have been shown to be initiated when SST reaches around 25 degrees C within the summer season. The prediction performance of LSTM-based neural network was evaluated using test data from 2017 and 2018, and the root-mean-squared-error (RMSE) and prediction accuracy were determined. Prediction for 2017 matched well with the test data; however, for 2018, the network predicted that a HAB would occur between July 19 and Sep. 03, with an RMSE of 0.233 and accuracy of 94.8 %, but the actual occurrence of HAB was between July 24 and Aug. 20. This study shows that the trained LSTM-based network would be useful for early prediction of the future red tide blooms in SSK.
ISSN
0749-0208
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/741
DOI
10.2112/SI90-029.1
Bibliographic Citation
JOURNAL OF COASTAL RESEARCH, v.90, no.sp1, pp.236 - 242, 2019
Publisher
COASTAL EDUCATION & RESEARCH FOUNDATION
Keywords
Harmful algal blooms (HABs); Margalefidinium polykrikoides; HAB occurrence prediction; LSTM neural network; South Sea of Korea (SSK)
Type
Article
Language
English
Document Type
Article
Publisher
COASTAL EDUCATION & RESEARCH FOUNDATION
Related Researcher
Research Interests

Coastal Remote Sensing,RS based Marine Surveillance System,GOCI Series Operation,연안 원격탐사,원격탐사기반 해양감시,천리안해양관측위성 운영

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