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Author

Im, Jungho
Intelligent Remote sensing and geospatial Information Systems (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Climate change

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Machine learning approaches to coastal water quality monitoring using GOCI satellite data

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Title
Machine learning approaches to coastal water quality monitoring using GOCI satellite data
Author
Kim, Yong HoonIm, JunghoHa, Ho KyungChoi, Jong-KukHa, Sunghyun
Keywords
chlorophyll-a concentration; GOCI; machine learning; suspended particulate matter; water quality
Issue Date
201403
Publisher
BELLWETHER PUBL LTD
Citation
GISCIENCE & REMOTE SENSING, v.51, no.2, pp.158 - 174
Abstract
Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and support vector regression (SVR) were evaluated for coastal water quality estimation. In situ measurements (63 samples) collected during four days in 2011 and 2012 were used as reference data. Due to the limited number of samples, leave-one-out cross validation (CV) was used to assess the performance of the water quality estimation models. Results show that SVR outperformed the other two machine learning approaches, yielding calibration R2 of 0.91 and CV root-mean-squared-error (RMSE) of 1.74 mg/m3 (40.7%) for chl-a, and calibration R2 of 0.98 and CV RMSE of 11.42 g/m3 (63.1%) for SPM when using GOCI-derived radiance data. Relative importance of the predictor variables was examined. When GOCI-derived radiance data were used, the ratio of band 2 to band 4 and bands 6 and 5 were the most influential input variables in predicting chl-a and SPM concentrations, respectively. Hourly available GOCI images were useful to discuss spatiotemporal distributions of the water quality parameters with tidal phases in the west coast of Korea.
URI
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DOI
http://dx.doi.org/10.1080/15481603.2014.900983
ISSN
1548-1603
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