Taorong Qiu
Department of Computer, Nanchang University, Jiangxi, Nanchang, 330031, China
Shanshan Zhang
Department of Computer, Nanchang University, Jiangxi, Nanchang, 330031, China
Hou Zhou
Department of Computer, Nanchang University, Jiangxi, Nanchang, 330031, China
Xiaoming Bai
Department of Computer, Nanchang University, Jiangxi, Nanchang, 330031, China
Ping Liu
Department of Computer, Nanchang University, Jiangxi, Nanchang, 330031, China
ABSTRACT
Aiming at the lack of highresolution and short-term (5, 5km and next 3 h) thunderstorm forecast model, the three thunderstorm forecast models based on Rough Sets (RS), Support Vector Machine (SVM) and RS-SVM are presented in this study through analyzing Rough set, Support machine vector and the characteristic of lightning forecasting. With the real data set, the models are tested and the experimental results are analyzed, which show that the RS-SVM based model is effective and moreover, acquires a higher forecast precision.
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How to cite this article
Taorong Qiu, Shanshan Zhang, Hou Zhou, Xiaoming Bai and Ping Liu, 2013. Application Study of Machine Learning in Lightning Forecasting. Information Technology Journal, 12: 6031-6037.
DOI: 10.3923/itj.2013.6031.6037
URL: https://scialert.net/abstract/?doi=itj.2013.6031.6037
DOI: 10.3923/itj.2013.6031.6037
URL: https://scialert.net/abstract/?doi=itj.2013.6031.6037
REFERENCES
- Lu, Z.Y., Y.Y. Li, J. Lu and Z.C. Zhao, 2008. Parameters optimization of RBF-SVM sand-dust storm forecasting model based on PSO. J. Tianjin Univ., 41: 413-418.
Direct Link - Ma, J., W. Fan and H.Y. Yuan, 2009. Application of SVM technology based on data field in thunderstorm report. Comput. Eng., 35: 263-265.
Direct Link - Wang, Z.H., W. Zhang and J. Zhu, 2011. A preliminary study on thunderstorm forecast based on LS-SVM. J. Trop. Meteorol., 27: 161-165.
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