• DocumentCode
    2456154
  • Title

    Flash floods forecasting without rainfalls forecasts by recurrent neural networks. Case study on the Mialet basin (Southern France)

  • Author

    Artigue, G. ; Johannet, Anne ; Borrell, V. ; Pistre, Severin

  • Author_Institution
    Centre des Mater. de Grande Diffusion, ECOLE DES MINES D´ALES, Ales, France
  • fYear
    2011
  • fDate
    19-21 Oct. 2011
  • Firstpage
    303
  • Lastpage
    310
  • Abstract
    The feasibility of flash flood prediction without rainfall forecasts nor previous discharge is considered. After a presentation of the important stakes involved in this task (23 fatalities in the Var event in June 2010, France) the important stage of variable and complexity selection is addressed for the small basin of Mialet (a part of the Gardon d´Anduze basin, in Southern France). Considering two architectures inspired from the multilayer perceptron, both designs and performances are presented and the model considering the linear and non-linear behaviors independently is proved to be the better. Generalization properties are assessed for four predictions up to two hours ahead thereby allowing an early warning of the population.
  • Keywords
    computational complexity; geophysics computing; multilayer perceptrons; rain; recurrent neural nets; weather forecasting; Mialet basin; Southern France; complexity selection; flash flood prediction; multilayer perceptron; rainfall forecasts; recurrent neural networks; Biological neural networks; Complexity theory; Discharges; Floods; Predictive models; Rain; Time factors; flash floods; forecasting; neural networks; recurrent model; ungauged basins;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on
  • Conference_Location
    Salamanca
  • Print_ISBN
    978-1-4577-1122-0
  • Type

    conf

  • DOI
    10.1109/NaBIC.2011.6089612
  • Filename
    6089612