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Cho, Kyung Hwa
Water-Environmental Informatics Lab (WEIL)
Research Interests
  • Water Quality Monitoring and Modeling, Water Treatment Process Modeling

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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
Author
Baek, Sang-SooPyo, JongCheolKwon, Yong SungChun, Seong-JunBaek, Seung HoAhn, Chi-YongOh, Hee-MockKim, Young OkCho, Kyung Hwa
Issue Date
2021-10
Publisher
FRONTIERS MEDIA SA
Citation
FRONTIERS IN MARINE SCIENCE, v.8, pp.729954
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.</p>
URI
https://scholarworks.unist.ac.kr/handle/201301/54878
URL
https://www.frontiersin.org/articles/10.3389/fmars.2021.729954/full
DOI
10.3389/fmars.2021.729954
ISSN
2296-7745
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UEE_Journal Papers
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