Mining geophysical parameters through decision-tree analysis to determine correlation with tropical cyclone development

Wenwen Li, Chaowei Yang, Donglian Sun

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Correlations between geophysical parameters and tropical cyclones are essential in understanding and predicting the formation of tropical cyclones. Previous studies show that sea surface temperature and vertical wind shear significantly influence the formation and frequent changes of tropical cyclones. This paper presents the utilization of a new approach, data mining, to discover the collective contributions to tropical cyclones from sea surface temperature, atmospheric water vapor, vertical wind shear, and zonal stretching deformation. A decision tree using the C4.5 algorithm was generated to illustrate the influence of geophysical parameters on the formation of tropical cyclone in weighted correlations. From the decision tree, we also induced decision rules to reveal the quantitative regularities and co-effects of [sea surface temperature, vertical wind shear], [atmospheric water vapor, vertical wind shear], [sea surface temperature, atmospheric water vapor, zonal stretching deformation], [sea surface temperature, vertical wind shear, atmospheric water vapor, zonal stretching deformation], and other combinations to tropical cyclone formation. The research improved previous findings in (1) preparing more precise criteria for future tropical cyclone prediction, and (2) applying data mining algorithms in studying tropical cyclones.

Original languageEnglish (US)
Pages (from-to)309-316
Number of pages8
JournalComputers and Geosciences
Volume35
Issue number2
DOIs
StatePublished - Feb 2009
Externally publishedYes

Keywords

  • Data mining
  • Hurricane
  • Natural disaster
  • Prediction

ASJC Scopus subject areas

  • Information Systems
  • Computers in Earth Sciences

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