GEOSTATIONARY OCEAN COLOR IMAGER(GOCI)를 이용한 호소의 클로로필-A와 부유물질 농도 예측을 위한 경험식과 통계모델의 활용

Abstract

Eutrophication is one of the most critical problems facing South Korea, and it has bacome a serious issue in four major river systems of the Korean Peninsula. Rapid measurement of chlorophyll-a(chl-a) and total suspended solid(TSS) at the initial bloom(HAB) events. However, because of spatial and temporal constraints, it is hard to obtain enough information about chl-a and TSS using Geostationary Ocean Color Image-satellite (GOCI) as an altenative approach in the Yeongam Reservoir(YA Reservoir). Additionally, support vector machine(SVM) was used as a supporting tool in this study for enhancement of efficiency of detecting by GOCInitial bloom(HAB) events. However, because of spatial and temporal constraints, it is hard to obtain enough information about chl-a and TSS using Geostationary Ocean Color Image-satellite (GOCI) as an altenative approach in the Yeongam Reservoir(YA Reservoir). Additionally, support vector machine(SVM) was used as a supporting tool in this study for enhancement of efficiency of detecting by GOCI