Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model
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Title
- Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model
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Author(s)
- Baek, Sang-Soo; Pyo, JongCheol; Kwon, Yong Sung; Chun, Seong-Jun; Baek, Seung Ho; Ahn, Chi-Yong; Oh, Hee-Mock; Kim, Young Ok; Cho, Kyung Hwa
- KIOST Author(s)
- Baek, Seung Ho(백승호); Kim, Young Ok(김영옥)
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Alternative Author(s)
- 백승호; 김영옥
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Publication Year
- 2021-10
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Abstract
- In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella. The classification and regression convolutional neural network (CNN) models are used for simulating the blooms. The classification CNN determines the bloom initiation while the regression CNN estimates the bloom density. GoogleNet and Resnet 101 are identified as the best structures for the classification and regression CNNs, respectively. The corresponding accuracy and root means square error values are determined as 96.8% and 1.20 [log(cells L-1)], respectively. The results obtained in this study reveal the simulated distribution to follow the Alexandrium catenella bloom. Moreover, Grad-CAM identifies that the salinity and temperature contributed to the initiation of the bloom whereas NH4-N influenced the growth of the bloom.
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ISSN
- 2296-7745
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URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/42117
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DOI
- 10.3389/fmars.2021.729954
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Bibliographic Citation
- FRONTIERS IN MARINE SCIENCE, v.8, 2021
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Publisher
- FRONTIERS MEDIA SA
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Keywords
- harmful algal blooms; deep learning; convolutional neural network; classification; regression
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Type
- Article
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Language
- English
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Document Type
- Article
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