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

Title
Assessment of modeling techniques for predicting Harmful Algal Bloom (HAB) outbreak using satellite data
Author(s)
신지선; 손영백; 김수미; 김근용; 유주형
KIOST Author(s)
Shin, Jisun(신지선)Son, Young Baek(손영백)Kim, Soo Mee(김수미)Kim, Keunyong(김근용)Ryu, Joo Hyung(유주형)
Publication Year
2018-11-05
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.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/22900
Bibliographic Citation
14th Pan Ocean Remote Sensing Conference, pp.48, 2018
Publisher
PORSC/KIOST
Type
Conference
Language
English
Publisher
PORSC/KIOST
Related Researcher
Research Interests

Coastal Remote Sensing,RS based Marine Surveillance System,GOCI Series Operation,연안 원격탐사,원격탐사기반 해양감시,천리안해양관측위성 운영

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