Open Access
E3S Web Conf.
Volume 7, 2016
3rd European Conference on Flood Risk Management (FLOODrisk 2016)
Article Number 18025
Number of page(s) 11
Section Forecasting and warning
Published online 20 October 2016
  1. Alfieri L., Salamon P., Pappenberger F., Wetterhall F. and Thielen J. (2012). Operational early warning systems for water-related hazards in Europe, Environmental Science & Policy, pp. 35–49. [CrossRef]
  3. Champion M. (1859). Les inondations en France du VIème siècle à nos jours, Eds Dalmont et Dunod.
  4. Lenne F. (2013). Crues de la Liane, Hem, Aa, Lys amont et plaine de la Lys du 29 octobre au 5 novembre 2012. Retour d’expérience SPC-SCHAPI 89.
  5. Azahaf O.-S. (2007). Création de réseaux de neurones pour la prévision des crues. Stage de M2 Mathématiques Appliquées de l’Université des Sciences et Technologie de Lille.
  6. Abrahart R. J. and See L. M. (2007). Neural network modelling of non-linear hydrological relationships, Hydrology and Earth System Sciences, 11(5), pp. 1563–1579, doi:10.5194/hess-11-1563-2007. [CrossRef]
  7. Dreyfus G. (2005). Neural Networks: Methodology and Applications, Softcover reprint of hardcover 1st ed. 2005 edition. Springer, Berlin; New York.
  8. Hornik K., Stinchcombe M., and White H. (1989). Multilayer Feedforward Networks Are Universal Approximators, Neural Networks 2, pp. 359–366. [CrossRef]
  9. Barron A.R. (1993). Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory IT- 39, pp. 930–945. [CrossRef] [MathSciNet]
  10. Nerrand O., Roussel-Ragot P., Personnaz L., Dreyfus G. and Marcos S. (1993). Neural networks and nonlinear adaptive filtering: unifying concepts and new algorithms. Neural Computation, 5(2), pp. 165–199. [CrossRef]
  11. Artigue G., Johannet A., Borrell V. and Pistre S. (2012). 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(11), pp. 3307–3324, doi:10.5194/nhess-12-3307-2012. [CrossRef]
  12. Geman S., Bienenstock E. and Doursat R., (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4 (1), pp. 1–58. [CrossRef]
  13. Stone M. (1974). Cross-validatory choice and assessment of statistical forecasting. Journal of the Royal Statistical Society, B 36, pp. 111–147.
  14. Kong-A-Siou L., Johannet A., Borrell V. and Pistre S. (2011). Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France). Journal of Hydrology. 403(3–4), pp. 367–380. [CrossRef]
  15. Kong-A-Siou L., Johannet A., Valérie B. E. and Pistre S. (2012). Optimization of the generalization capability for rainfall–runoff modeling by neural networks: the case of the Lez aquifer (southern France). Environmental Earth Sciences, 65(8), pp. 2365–2375, doi:10.1007/s12665-011-1450-9. [CrossRef]
  16. Schoups G., Van de Giesen N. C. and Savenije H. G. (2008). Model complexity control for hydrologic prediction, Water Resource Research, 44(12), W00B03, doi:10.1029/2008WR006836,. [CrossRef]
  17. Sjöberg J. and Ljung L. (1994). Overtraining, regularization and searching for minimum in neural networks, Preprint IFAC Symposium on Adaptive Systems in Control and Signal Processing.
  18. Hagan M. T. and Menhaj M. B. (1994). Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Netw., 5(6), pp. 989–993, doi:10.1109/72.329697. [CrossRef] [PubMed]
  19. Darras T., Johannet A., Vayssade B., Kong-A-Siou L. and Pistre S. (2014). Influence of the Initialization of Multilayer Perceptron for Flash Floods Forecasting: How Designing a Robust Model, Granada, ITISE Conference, Ruiz, I. R., and Garcia, G. R., Eds, pp. 687–698.
  20. Toukourou M., Johannet A., Dreyfus G. and Ayral P.-A. (2011). Rainfall-runoff modeling of flash floods in the absence of rainfall forecasts: the case of “Cévenol flash floods”. Journal of Applied Intelligence, 35, 2, pp. 1078–189. [CrossRef]
  21. Kong-A-Siou L., Johannet A., Estupina V. and Pistre S. (2015). Neural networks for karst groundwater management: case of the Lez spring (Southern France). Environmental Earth Sciences, 74(12) pp. 7617–7632. [CrossRef]
  22. Nash J.E. and Sutcliffe J.V. (1970). River flow forecasting through conceptual models part I–A discussion of principles. Journal of hydrology, 10(3), 282–290. [CrossRef]
  23. Kitadinis P.K. and Bras R. (1980). Real time forecasting with a conceptual hydrologic model, applications and results, Water Resources Research, 16, n° 6, pp. 1034–1044,
  24. Oudin L., Michel C. and Anctil F. (2005). Which potential evapotranspiration input for a lumped rainfall-runoff model? Part 1—Can rainfall-runoff models effectively handle detailed potential evapotranspiration inputs? Journal of Hydrology 303, pp. 275–289. [CrossRef]
  25. Oudin L., Hervieu F., Michel C., Perrin C., Andréassian V., Anctil F. and Loumagne C. (2005). Which potential evapotranspiration input for a lumped rainfall–runoff model? Part 2—Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling. Journal of Hydrology 303, pp. 290–306. [CrossRef]
  26. Kong-A-Siou L., Fleury P., Johannet A., Borrell Estupina V., Pistre S. and Dörfliger N. (2014). Performance and complementarity of two systemic models (reservoir and neural networks) used to simulate spring discharge and piezometry for a karst aquifer. Journal of Hydrology, 519(D), pp. 3178–3192. [CrossRef]

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