TY - JOUR
T1 - Mining geophysical parameters through decision-tree analysis to determine correlation with tropical cyclone development
AU - Li, Wenwen
AU - Yang, Chaowei
AU - Sun, Donglian
N1 - Funding Information:
Research reported is supported by a 973 Grant # 2006CB701306 and NASA Grants # NNG04GH96A and NNX07AD99G. Dr. Eric Grunsky, the Editor-in-Chief of Computers and Geosciences, Dr. Karl Benedict, and the anonymous reviewers provided constructive comments in improving the paper.
PY - 2009/2
Y1 - 2009/2
N2 - 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.
AB - 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.
KW - Data mining
KW - Hurricane
KW - Natural disaster
KW - Prediction
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U2 - 10.1016/j.cageo.2008.02.032
DO - 10.1016/j.cageo.2008.02.032
M3 - Article
AN - SCOPUS:58349106193
SN - 0098-3004
VL - 35
SP - 309
EP - 316
JO - Computers and Geosciences
JF - Computers and Geosciences
IS - 2
ER -