Assessment of modeling techniques for predicting Harmful Algal Bloom (HAB) outbreak using satellite data

DC Field Value Language
dc.contributor.author 신지선 -
dc.contributor.author 손영백 -
dc.contributor.author 김수미 -
dc.contributor.author 김근용 -
dc.contributor.author 유주형 -
dc.date.accessioned 2020-07-15T09:53:58Z -
dc.date.available 2020-07-15T09:53:58Z -
dc.date.created 2020-02-11 -
dc.date.issued 2018-11-05 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/22900 -
dc.description.abstract Since 1995, Harmful Algal Bloom (HAB) by Cochlodinium polykrikoides has frequently occurred in the South Sea of Korea (SSK). The HAB occurrence is related to various factors such as physical and biological, so it is very difficult to predict the HAB occurrence. To reduce the damage, it is essential to make a preliminary forecast of the HAB occurrence through correlation analysis of the HAB occurrence factors. The purpose of this study is to perform and assess the HAB occurrence prediction using modeling techniques. We used satellite data such as Photosynthetically Available Radiation (PAR) and Sea Surface Temperature (SST) which are physical factors of the HAB occurrence. In order to predict the HAB occurrence, we analyzed the correlation of PAR and SST data with past HAB occurrence time (1998-2018) and investigated the performance of the prediction models based on data curves, statistical analysis method, and learning accumulated database. These results are expected to be useful data for predicting future HAB occurrence.ict the HAB occurrence. To reduce the damage, it is essential to make a preliminary forecast of the HAB occurrence through correlation analysis of the HAB occurrence factors. The purpose of this study is to perform and assess the HAB occurrence prediction using modeling techniques. We used satellite data such as Photosynthetically Available Radiation (PAR) and Sea Surface Temperature (SST) which are physical factors of the HAB occurrence. In order to predict the HAB occurrence, we analyzed the correlation of PAR and SST data with past HAB occurrence time (1998-2018) and investigated the performance of the prediction models based on data curves, statistical analysis method, and learning accumulated database. These results are expected to be useful data for predicting future HAB occurrence. -
dc.description.uri 1 -
dc.language English -
dc.publisher PORSC/KIOST -
dc.relation.isPartOf 14th Pan Ocean Remote Sensing Conference -
dc.title Assessment of modeling techniques for predicting Harmful Algal Bloom (HAB) outbreak using satellite data -
dc.type Conference -
dc.citation.conferenceDate 2018-11-04 -
dc.citation.conferencePlace KO -
dc.citation.endPage 48 -
dc.citation.startPage 48 -
dc.citation.title 14th Pan Ocean Remote Sensing Conference -
dc.contributor.alternativeName 신지선 -
dc.contributor.alternativeName 손영백 -
dc.contributor.alternativeName 김수미 -
dc.contributor.alternativeName 김근용 -
dc.contributor.alternativeName 유주형 -
dc.identifier.bibliographicCitation 14th Pan Ocean Remote Sensing Conference, pp.48 -
dc.description.journalClass 1 -
Appears in Collections:
Marine Industry Research Division > Maritime ICT & Mobility Research Department > 2. Conference Papers
Marine Digital Resources Department > Korea Ocean Satellite Center > 2. Conference Papers
Jeju Research Institute > Tropical & Subtropical Research Center > 2. Conference Papers
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