DocumentCode :
2098858
Title :
Identification of Severe Precipitation Radar Echo Reflectivity with Back-Propagation ANN
Author :
Wang, Jing ; Gao, Yuchun ; Xiong, Yi ; Cheng, Minghu ; Zhu, Shuai
Author_Institution :
Sch. of Inf. & Electron. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China
Volume :
2
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
594
Lastpage :
597
Abstract :
In this thesis, the radar echo reflectivity of severe precipitation in the flood season of Changjiang-Huaihe area was identified by a Back-Propagation (BP) Model of Artificial Neural Network (ANN). The trained network was applied in a precipitation progress in the same area in 2001. The results illustrate that: the single hide-layer BP ANN can be used to identify the target radar echo at a high succeed rate. It is also validated that the performance of the network is influenced by following factors: the quality and input sequence of the training sample, the framework of hide layer and the learning rate.
Keywords :
atmospheric precipitation; backpropagation; echo; geophysical signal processing; neural nets; radar cross-sections; radar signal processing; radar target recognition; Changjiang-Huaihe area; artificial neural network; backpropagation ANN; backpropagation model; precipitation radar echo reflectivity; target radar echo; trained network; Artificial neural networks; Computer networks; Computer science; Economic forecasting; Meteorological radar; Neural networks; Radar theory; Rain; Reflectivity; Spaceborne radar; ANN; Radar; Severe Precipitation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3746-7
Type :
conf
DOI :
10.1109/ISCSCT.2008.365
Filename :
4731694
Link To Document :
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