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

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

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dc.contributor.author Kim, Yong Hoon ko
dc.contributor.author Im, Jungho ko
dc.contributor.author Ha, Ho Kyung ko
dc.contributor.author Choi, Jong-Kuk ko
dc.contributor.author Ha, Sunghyun ko
dc.date.available 2014-05-07T02:05:28Z -
dc.date.created 2014-05-07 ko
dc.date.issued 2014-03 ko
dc.identifier.citation GISCIENCE & REMOTE SENSING, v.51, no.2, pp.158 - 174 ko
dc.identifier.issn 1548-1603 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/4484 -
dc.description.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. ko
dc.description.statementofresponsibility close -
dc.language 영어 ko
dc.publisher BELLWETHER PUBL LTD ko
dc.title Machine learning approaches to coastal water quality monitoring using GOCI satellite data ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-84898764774 ko
dc.identifier.wosid 000333870200004 ko
dc.type.rims ART ko
dc.description.wostc 0 *
dc.description.scopustc 1 *
dc.date.tcdate 2014-10-18 *
dc.date.scptcdate 2014-08-20 *
dc.identifier.doi 10.1080/15481603.2014.900983 ko
dc.identifier.url http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84898764774 ko
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