• DocumentCode
    2363223
  • 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
  • fYear
    2011
  • fDate
    28-30 Sept. 2011
  • Firstpage
    710
  • Lastpage
    713
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    ICT Convergence (ICTC), 2011 International Conference on
  • Conference_Location
    Seoul
  • Print_ISBN
    978-1-4577-1267-8
  • Type

    conf

  • DOI
    10.1109/ICTC.2011.6082682
  • Filename
    6082682