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Short-imminent heavy rainfall is a weather phenomenon with large rainfall intensity in a short period of time, and its effective prediction can minimize the damage caused by short-imminent heavy rainfall. Fujian province is a region with short-imminent heavy rainfall. The accurate identification of short-imminent heavy rainfall has been the key research goal of Fujian provincial meteorological researchers. In this paper, based on ECMWF numerical forecast products and ground observed rainfall data, RBF interpolation is adopted to achieve data assimilation, and data are divided according to the ground observed rainfall data. Due to the unbalanced distribution of samples, unlike the traditional oversampling method, this paper uses the Synthetic Minority Oversampling Technique (SMOTE) to generate short-imminent heavy rainfall data, combined with Focal loss and DBNs to construct the shortimminent heavy rainfall classification model FL-DBNs. The experimental results show that the TS value of FL-DBNs is increased by 47.8% in the cumulative rainfall of 1-30mm, and the PO and ETS are greatly improved. Accumulated rainfall of more than 30mm has reached a certain classification requirements, and solved the problem that it is difficult for ECMWF to predict rainfall above 30mm, which has certain guiding significance for short-imminent heavy rainfall classification. © 2019 IEEE.
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Year： 2019
Page： 1340-1345
Language： English
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SCOPUS Cited Count： 1
ESI Highly Cited Papers on the List： 0 Unfold All
WanFang Cited Count：
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30 Days PV： 3
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