Articles | Volume 19, issue 10
https://doi.org/10.5194/hess-19-4397-2015
https://doi.org/10.5194/hess-19-4397-2015
Research article
 | 
30 Oct 2015
Research article |  | 30 Oct 2015

Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France)

T. Darras, V. Borrell Estupina, L. Kong-A-Siou, B. Vayssade, A. Johannet, and S. Pistre

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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. Darras, F. Raynaud, V. Borrell Estupina, L. Kong-A-Siou, S. Van-Exter, B. Vayssade, A. Johannet, and S. Pistre
Proc. IAHS, 369, 43–48, https://doi.org/10.5194/piahs-369-43-2015,https://doi.org/10.5194/piahs-369-43-2015, 2015

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Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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Cited articles

Abrahart, R. J. and See, L. M.: Neural network modelling of non-linear hydrological relationships, Hydrol. Earth Syst. Sci., 11, 1563–1579, https://doi.org/10.5194/hess-11-1563-2007, 2007.
Anctil, F., Lauzon, N., and Filion, M.: Added gains of soil moisture content observations for streamflow predictions using neural networks, J. Hydrol., 359, 225–234, https://doi.org/10.1016/j.jhydrol.2008.07.003, 2008.
Artigue, G., Johannet, A., Borrell, V., and Pistre, S.: Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France), Nat. Hazards Earth Syst. Sci., 12, 3307–3324, https://doi.org/10.5194/nhess-12-3307-2012, 2012.
Bailly-Comte, V., Borrell-Estupina, V., Jourde, H., and Pistre, S.: A conceptual semidistributed model of the Coulazou River as a tool for assessing surface water-karst groundwater interactions during flood in Mediterranean ephemeral rivers, Water Resour. Res., 48, W09534, https://doi.org/10.1029/2010WR010072, 2012.
Bakalowicz, M.: Karst groundwater: a challenge for new resources, Hydrogeol. J., 13, 148–160, https://doi.org/10.1007/s10040-004-0402-9, 2005.
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Short summary
Flash floods are important hazards in urbanised zone and constitute important human and financial stakes. This paper applies a novel methodology, KnoX, dedicated to extract knowledge from a neural network model. It was shown that KnoX method could help to better characterize processes of both surface and underground floods. A case study is chosen in France: the Lez karst hydrosystem whose river crosses the city of Montpellier (400 000 inhabitants). Results will help flood warning services.