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Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River

Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affec...

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Autores principales: Ahn, Jung Min, Kim, Byungik, Jong, Jaehun, Nam, Gibeom, Park, Lan Joo, Park, Sanghyun, Kang, Taegu, Lee, Jae-Kwan, Kim, Jungwook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828484/
https://www.ncbi.nlm.nih.gov/pubmed/33451010
http://dx.doi.org/10.3390/s21020530
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author Ahn, Jung Min
Kim, Byungik
Jong, Jaehun
Nam, Gibeom
Park, Lan Joo
Park, Sanghyun
Kang, Taegu
Lee, Jae-Kwan
Kim, Jungwook
author_facet Ahn, Jung Min
Kim, Byungik
Jong, Jaehun
Nam, Gibeom
Park, Lan Joo
Park, Sanghyun
Kang, Taegu
Lee, Jae-Kwan
Kim, Jungwook
author_sort Ahn, Jung Min
collection PubMed
description Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affect long-term predictions, the initial cyanobacterial distribution in water is particularly important for short-term predictions. Point-based observation data have typically been used for cyanobacteria prediction of initial conditions. These initial conditions are determined through the linear interpolation of point-based observation data and may differ from the actual cyanobacteria distribution. This study presents an optimal method of applying hyperspectral images to establish the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model initial conditions. Utilizing hyperspectral images to determine the EFDC-NIER model initial conditions involves four steps that are performed sequentially and automated in MATLAB. The EFDC-NIER model is established using three grid resolution cases for the Changnyeong-Haman weir section of the Nakdong River Basin, where Microcystis dominates during the summer (July to September). The effects of grid resolution on (1) water quality modeling and (2) initial conditions determined using cumulative distribution functions are evaluated. Additionally, the differences in Microcystis values are compared when applying initial conditions using hyperspectral images and point-based evaluation data. Hyperspectral images allow detailed initial conditions to be applied in the EFDC-NIER model based on the plane-unit cyanobacterial information observed in grids, which can reduce uncertainties in water quality (cyanobacteria) modeling.
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spelling pubmed-78284842021-01-25 Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River Ahn, Jung Min Kim, Byungik Jong, Jaehun Nam, Gibeom Park, Lan Joo Park, Sanghyun Kang, Taegu Lee, Jae-Kwan Kim, Jungwook Sensors (Basel) Article Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affect long-term predictions, the initial cyanobacterial distribution in water is particularly important for short-term predictions. Point-based observation data have typically been used for cyanobacteria prediction of initial conditions. These initial conditions are determined through the linear interpolation of point-based observation data and may differ from the actual cyanobacteria distribution. This study presents an optimal method of applying hyperspectral images to establish the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model initial conditions. Utilizing hyperspectral images to determine the EFDC-NIER model initial conditions involves four steps that are performed sequentially and automated in MATLAB. The EFDC-NIER model is established using three grid resolution cases for the Changnyeong-Haman weir section of the Nakdong River Basin, where Microcystis dominates during the summer (July to September). The effects of grid resolution on (1) water quality modeling and (2) initial conditions determined using cumulative distribution functions are evaluated. Additionally, the differences in Microcystis values are compared when applying initial conditions using hyperspectral images and point-based evaluation data. Hyperspectral images allow detailed initial conditions to be applied in the EFDC-NIER model based on the plane-unit cyanobacterial information observed in grids, which can reduce uncertainties in water quality (cyanobacteria) modeling. MDPI 2021-01-13 /pmc/articles/PMC7828484/ /pubmed/33451010 http://dx.doi.org/10.3390/s21020530 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ahn, Jung Min
Kim, Byungik
Jong, Jaehun
Nam, Gibeom
Park, Lan Joo
Park, Sanghyun
Kang, Taegu
Lee, Jae-Kwan
Kim, Jungwook
Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River
title Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River
title_full Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River
title_fullStr Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River
title_full_unstemmed Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River
title_short Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River
title_sort predicting cyanobacterial blooms using hyperspectral images in a regulated river
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828484/
https://www.ncbi.nlm.nih.gov/pubmed/33451010
http://dx.doi.org/10.3390/s21020530
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