File Download

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

조경화

Cho, Kyung Hwa
Water-Environmental Informatics Lab.
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Author(s)
Kwon, Yong SungBaek, Seung HoLim, Young KyunPyo, JongCheolLigaray, MayzoneePark, YongeunCho, Kyung Hwa
Issued Date
2018-08
DOI
10.3390/w10081020
URI
https://scholarworks.unist.ac.kr/handle/201301/25053
Fulltext
https://www.mdpi.com/2073-4441/10/8/1020
Citation
WATER, v.10, no.8, pp.1020
Abstract
Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms.
Publisher
MDPI
ISSN
2073-4441
Keyword (Author)
harmful algal bloomsremote sensingLandsat-8 Operational Land Imagermachine learning
Keyword
HARMFUL ALGAL BLOOMSARTIFICIAL NEURAL-NETWORKWATER-QUALITY PARAMETERSRED TIDESCOCHLODINIUM-POLYKRIKOIDESECOLOGICAL ROLESSATELLITE DATAINLAND WATERSKOREAPREDICTION

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.