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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 369
Proc. IAHS, 369, 43–48, 2015
https://doi.org/10.5194/piahs-369-43-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 369, 43–48, 2015
https://doi.org/10.5194/piahs-369-43-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  11 Jun 2015

11 Jun 2015

Neural network modeling and geochemical water analyses to understand and forecast karst and non-karst part of flash floods (case study on the Lez river, Southern France)

T. Darras1,2, F. Raynaud2, V. Borrell Estupina2, L. Kong-A-Siou3, S. Van-Exter4, B. Vayssade1, A. Johannet1, and S. Pistre2 T. Darras et al.
  • 1École des mines d'Alès, 6 avenue de Clavières, 30319 Alès CEDEX, France
  • 2Hydrosciences Montpellier, Université de Montpelier II, 2 Place Eugène Bataillon, 34095 Montpellier CEDEX 5, France
  • 3MAYANE, 173 chemin de Fescau, 34980 Montferrier-sur-Lez, France
  • 4Hydrosciences Montpellier, CNRS, Montpellier, 2 Place Eugène Bataillon, 34095 Montpellier CEDEX 5, France

Abstract. Flash floods forecasting in the Mediterranean area is a major economic and societal issue. Specifically, considering karst basins, heterogeneous structure and nonlinear behaviour make the flash flood forecasting very difficult. In this context, this work proposes a methodology to estimate the contribution from karst and non-karst components using toolbox including neural networks and various hydrological methods. The chosen case study is the flash flooding of the Lez river, known for his complex behaviour and huge stakes, at the gauge station of Lavallette, upstream of Montpellier (400 000 inhabitants). After application of the proposed methodology, discharge at the station of Lavallette is spited between hydrographs of karst flood and surface runoff, for the two events of 2014. Generalizing the method to future events will allow designing forecasting models specifically for karst and surface flood increasing by this way the reliability of the forecasts.

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