• 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