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

Cited 8 time in WEB OF SCIENCE Cited 10 time in Scopus
Title
Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models
Author(s)
Kwon, Yong Sung; Baek, Seung Ho; Lim, Young Kyun; Pyo, JongCheol; Ligaray, Mayzonee; Park, Yongeun; Cho, Kyung Hwa
KIOST Author(s)
Baek, Seung Ho(백승호)Lim, Young kyun(임영균)
Publication Year
2018-08
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.
ISSN
2073-4441
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/861
DOI
10.3390/w10081020
Bibliographic Citation
WATER, v.10, no.8, 2018
Publisher
MDPI
Subject
HARMFUL ALGAL BLOOMS; ARTIFICIAL NEURAL-NETWORK; WATER-QUALITY PARAMETERS; RED TIDES; COCHLODINIUM-POLYKRIKOIDES; ECOLOGICAL ROLES; SATELLITE DATA; INLAND WATERS; KOREA; PREDICTION
Keywords
harmful algal blooms; remote sensing; Landsat-8 Operational Land Imager; machine learning
Type
Article
Language
English
Document Type
Article
Publisher
MDPI
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