2019
DOI: 10.2112/si90-029.1
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Early Prediction of Margalefidinium polykrikoides Bloom Using a LSTM Neural Network Model in the South Sea of Korea

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Cited by 20 publications
(4 citation statements)
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“…It controls the transmission by designing "gated states" through special memory units that remember important feature information and forget unimportant ones. This special design can be well done for simulation and prediction of longterm time series data [11], [240], [241]. If all influences are fed into the LSTM model, the neural network will generate a lot of noise, which will interfere with the efficiency of the model in learning useful information.…”
Section: ) Based On Machine Learning (Ml) Modelsmentioning
confidence: 99%
“…It controls the transmission by designing "gated states" through special memory units that remember important feature information and forget unimportant ones. This special design can be well done for simulation and prediction of longterm time series data [11], [240], [241]. If all influences are fed into the LSTM model, the neural network will generate a lot of noise, which will interfere with the efficiency of the model in learning useful information.…”
Section: ) Based On Machine Learning (Ml) Modelsmentioning
confidence: 99%
“…In these cases, empirical statistical models can be simpler alternatives but successful for HAB forecasting. In [74], an LSTM RNN is presented as a way to predict the occurrence time of Margalefidinium polykrikoides blooms in South Sea of Korea. Satellite data is used to extract sea surface temperature and photosynthetically available radiation, factors known to be related to HABs occurrence.…”
Section: A Algal Bloomsmentioning
confidence: 99%
“…The detection and analysis of the green and golden tides in the Yellow Sea and the East China Sea have been performed (Chen et al, 2019b;Kim et al, 2019a;Liang et al, 2019;Min et al, 2019;Wang et al, 2019) and the detection and prediction of the red tide have been studied (Kim et al, 2019c;Liu et al, 2019;Park et al, 2019a;Shin et al, 2019). And the environmental monitoring studies were also conducted from the OISST, ARGO, MODIS, Landsat, and TerraSAR-X images (Baek and Moon, 2019;Chen et al, 2019a;Eom et al, 2019;Hong et al, 2019;Jeong et al, 2019;Jung et al, 2019;Lee et al, 2019a;Li et al, 2019;Ma et al, 2019;Mu et al, 2019;Sun et al, 2019;Tong et al, 2019;Qing, Hao, and Bao, 2019;Ren et al, 2019b;Xiao, Zhang, and Qin, 2019;Zhang et al, 2019aZhang et al, , 2019b The research topics of the oil spill, typhoon, flood, and nuclear radiation emergent have been carried out by using optical and SAR images (Bing et al, 2019;Jin et al, 2019;Kim and Moon, 2019;Park et al, 2019b;Syifa et al, 2019;Yang et al, 2019).…”
Section: Previous Special Issue Related To Geospatial Research Of Coamentioning
confidence: 99%