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Zhejiang","award":["41806004"],"award-info":[{"award-number":["41806004"]}]},{"name":"Marine Sciences in the First-Class Subjects of Zhejiang","award":["2020J00007"],"award-info":[{"award-number":["2020J00007"]}]},{"name":"Marine Sciences in the First-Class Subjects of Zhejiang","award":["DH-2022KF0208"],"award-info":[{"award-number":["DH-2022KF0208"]}]},{"name":"Marine Sciences in the First-Class Subjects of Zhejiang","award":["OFMS006"],"award-info":[{"award-number":["OFMS006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, the abnormal distribution of which is closely related to the hazards of red tides. Thus, the accurate prediction of its concentration in the East China Sea (ECS) is greatly important for preventing water eutrophication and protecting the coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011\u20132020) satellite-observed chlorophyll-a data and logarithmic data were used as the long short-term memory (LSTM) neural network training datasets in this study. The 2021 data were used for comparison to prediction results. The past 15 days\u2019 data were used to predict the concentration of chlorophyll-a for the five following days. Results showed that the predictions obtained by both pre-processing methods could simulate the seasonal distribution of the Chl-a concentration in the ECS effectively. Moreover, the prediction performance of the model driven by the original values was better in the medium- and low-concentration regions. However, in the high-concentration region, the prediction of extreme concentrations by the two data-driven LSTM models showed underestimation, considering that the prediction performance of the model driven by the original values was better. Results of sensitivity experiments showed that the prediction accuracy of the model decreased considerably when the backward prediction time step increased. In this study, the neural network was driven only by chlorophyll-a, whose concentration in the ECS was forecasted, and the effect of other relevant marine elements on Chl-a was not considered, which is the current weakness of this study.<\/jats:p>","DOI":"10.3390\/rs14215461","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"5461","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea"],"prefix":"10.3390","volume":"14","author":[{"given":"Haobin","family":"Cen","sequence":"first","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Han","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiayan","family":"Lin","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316300, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Jia","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiyan","family":"Ji","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9043-9484","authenticated-orcid":false,"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316300, China"},{"name":"Science Foundation of Donghai Laboratory, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.ecss.2007.05.015","article-title":"Spatial and Temporal Variability of Chlorophyll and Primary Productivity in Surface Waters of Southern Chile (41.5\u201343 S)","volume":"74","author":"Iriarte","year":"2007","journal-title":"Estuar. 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