• DocumentCode
    420329
  • Title

    A connectionist approach to thunderstorm forecasting

  • Author

    Choudhury, Swati ; Mitra, Sushmita ; Chakraborty, Himadri

  • Author_Institution
    Machine Intelligence Unit, Indian Stat. Inst., Calcutta, India
  • Volume
    1
  • fYear
    2004
  • fDate
    27-30 June 2004
  • Firstpage
    330
  • Abstract
    Thunderstorms are considered to be global phenomena, as they may occur anywhere in the world, at any instant. Lightning, damaging straight-line wind, large sized hails, heavy precipitation and flooding are some damage-prone factors associated with these storms. Depending on their types (specially the supercell thunderstorm, having the tendency to form tornadoes), some of them may possess great potentiality to produce serious damages to human life and property. Now-a-days, due to rapid technical development, many sophisticated instruments (such as Doppler radar, Satellite, Radiosonde, etc.) are available to record weather data. Efforts are being made to use these data by designing models based on statistical, mathematical, and soft computing techniques, in order to forecast damaging weather conditions with greater reliability.
  • Keywords
    backpropagation; floodlighting; forecasting theory; multilayer perceptrons; statistical analysis; thunderstorms; weather forecasting; ANN; India; artificial neural network; backpropagation learning; coastal region; connectionist method; damage prone factors; flooding; heavy precipitation; lightning; mathematical techniques; meteorology; radiocommunication; reliability; research and development; rules extraction; seasonal thunderstorms; soft computing techniques; statistical techniques; straight line wind damage; supercell thunderstorm; thunderstorm forecasting; weather data; weather parameters; Doppler radar; Floods; Humans; Instruments; Lightning; Satellite broadcasting; Storms; Tornadoes; Weather forecasting; Wind forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the
  • Print_ISBN
    0-7803-8376-1
  • Type

    conf

  • DOI
    10.1109/NAFIPS.2004.1336302
  • Filename
    1336302