Next Article in Journal
Combination of Measurement Methods for a Wide-Range Description of Hydraulic Soil Properties
Previous Article in Journal
GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Water 2018, 10(8), 1020; https://doi.org/10.3390/w10081020

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

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
2
South Sea Research Institute, Korea Institute of Ocean Science and Technology, Geoje 53201, Korea
3
Department of Oceanography and Ocean Environmental Sciences, Chungnam National University, Daejon 34134, Korea
4
School of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea
*
Authors to whom correspondence should be addressed.
Received: 13 May 2018 / Revised: 15 July 2018 / Accepted: 19 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)
Full-Text   |   PDF [2946 KB, uploaded 7 August 2018]   |  

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. View Full-Text
Keywords: harmful algal blooms; remote sensing; Landsat-8 Operational Land Imager; machine learning harmful algal blooms; remote sensing; Landsat-8 Operational Land Imager; machine learning
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Kwon, Y.S.; Baek, S.H.; Lim, Y.K.; Pyo, J.; Ligaray, M.; Park, Y.; Cho, K.H. Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models. Water 2018, 10, 1020.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top