Title :
Prediction of red tide blooms using decision tree model
Author :
Park, Sun ; Jung, Min A. ; Lee, Seong Ro ; Pyo, Se Jun ; Park, Jae Hai ; Kim, Kong Soung ; Park, Yinsoo
Author_Institution :
Inst. of Inf. Sci. & Eng. Res., Mokpo Nat. Univ., Mokpo, South Korea
Abstract :
A red tide damages sea farming on the coast of many countries, and generally has a bad influence on the coastal environment and sea ecosystem. To enhance the prediction of red tide blooms, this paper proposes a red tide prediction method that uses decision tree. The proposed method improves the precision of prediction because the decision tree classifier is enhanced by the modeled data of the proposed preprocessing. The experimental results demonstrate that the proposed method achieves a better red tide prediction performance than other classifiers.
Keywords :
aquaculture; decision trees; pattern classification; prediction theory; coastal environment; decision tree classifier; decision tree model; red tide blooms prediction; sea ecosystem; sea farming; Aquaculture; Data preprocessing; Decision trees; Predictive models; Temperature distribution; Tides; Red tide blooms; decision tree; model; prediction;
Conference_Titel :
ICT Convergence (ICTC), 2011 International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4577-1267-8
DOI :
10.1109/ICTC.2011.6082682