2011Journal of the Institute of Electronics Engineers of KoreaRequires access

Red Tide Prediction using Neural Network and SVM

Sun Park, Kyung-Jun Kim, Jinseok Lee, Seong-Ro Lee

Open publisher page 3 citations

Abstract

There have been many studies on red tide because of increasing of damage to sea farming by a red tide blooms of harmful algae. The studies of red tide have mostly focused chemical properties and investigation of biological cause. If we can predict the occurrence of red tide, we will be able to minimize the damage of red tide. However, internal study of prediction of red tide blooms is only classification method that is still insufficient for red tide blooms forecast. In this paper, we proposed the red tide blooms prediction method using neural network and SVM.

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What this paper is about

There have been many studies on red tide because of increasing of damage to sea farming by a red tide blooms of harmful algae. The studies of red tide have mostly focused chemical properties and investigation of biological cause. If we can predict the occurrence of red tide, we will be able to minimize the damage of red tide. However, internal study of prediction of red tide blooms is only classification method that is still insufficient for red tide blooms forecast. In this paper, we proposed the red tide blooms prediction method using neural network and SVM.

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Available abstract

There have been many studies on red tide because of increasing of damage to sea farming by a red tide blooms of harmful algae. The studies of red tide have mostly focused chemical properties and investigation of biological cause. If we can predict the occurrence of red tide, we will be able to minimize the damage of red tide. However, internal study of prediction of red tide blooms is only classification method that is still insufficient for red tide blooms forecast. In this paper, we proposed the red tide blooms prediction method using neural network and SVM.

Key concepts: Red tide, Algal bloom, Support vector machine, Artificial neural network, Oceanography, Environmental science, Meteorology, Computer science

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