M. Rouzeau, M. Xavier, and J. C. Pauc, Retour d'expériences des inondations survenues dans le departement du Var les 15 et 16 juins 2010, 2010.

J. Roberts, S. , and W. Penny, Neural Networks: Friends or Foes? Sensor Review, vol.17, 1981.

M. Toukourou, A. Johannet, G. Dreyfus, and P. A. Ayral, Rainfall-runoff Modeling of Flash Floods in the Absence of Rainfall Forecasts: the Case of, App. Intelligence, vol.35, pp.178-189, 2011.

G. Artigue, Flash Flood Forecasting in Poorly Gauged Basins Using Neural Networks: Case Study of the Gardon de Mialet Basin, NHESS, vol.12, issue.11, pp.3307-3331, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02365588

Y. Oussar and G. Dreyfus, How to Be a Gray Box: Dynamic Semi-Physical Modeling, Neural Networks, vol.14, issue.9, p.96, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00922197

A. Johannet, D. Vayssade, and . Bertin, Neural Networks: From Black Box towards Transparent Box -Application to Evapotranspiration Modelling, Int. Journal of Comp. Int, vol.24, issue.1, p.162, 2007.

L. Kong-a-siou, KnoX method, or Knowledge eXtraction from neural network model. Case study on the Lez karst aquifer (southern France), J. Hydrol, vol.507, pp.19-32
URL : https://hal.archives-ouvertes.fr/hal-02410268

T. Darras, Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France) Hydrol, Earth Syst. Sci, vol.19, pp.4397-4410, 2015.

T. Darras, A. Johannet, B. Vayssade, L. Kong-a-siou, S. Pistre et al., Influence of the Initialization of Multilayer Perceptron for Flash Floods Forecasting: How Designing a Robust Model, pp.687-698, 2014.

G. Dreyfus, Neural networks, methodology and applications, 2005.

K. Hornik, M. Stinchcombe, and H. White, Multilayer Feedforward Networks Are Universal Approximators, Neural Networks, vol.2, issue.5, pp.359-66, 1989.

A. R. Barron, Universal Approximation Bounds for Superpositions of a Sigmoidal Function, IEEE Trans. Inf. Theor, vol.39, issue.3, pp.930-975, 1993.

O. Nerrand, P. Roussel-ragot, L. Personnaz, G. Dreyfus, and S. Marcos, Neural Networks and Nonlinear Adaptive Filtering: Unifying Concepts and New Algorithms, Neural Comp, vol.5, issue.2, pp.165-99, 1993.

S. Geman, E. Bienenstock, and R. Doursat, Neural Networks and the Bias/Variance Dilemma, Neural Computation, vol.4, issue.1, pp.1-58, 1992.

J. Sjöberg, Nonlinear Black-Box Modeling in System Identification: A Unified Overview, Automatica, vol.31, issue.12, pp.1691-1724, 1995.

M. Stone, Cross-Validatory Choice and Assessment of Statistical Predictions (With Discussion), Journal of the Royal Statistical Society: Series B (Methodological), vol.38, issue.1, pp.102-102, 1976.

T. G. Dietterich, Ensemble Methods in Machine Learning, First Int. Workshop on Multiple Classifier Systems, pp.1-15, 2000.

J. E. Nash and J. V. Sutcliffe, River Flow Forecasting through Conceptual Models Part I -A Discussion of Principles, Journal of Hydrology, vol.10, issue.3, pp.282-90, 1970.

G. Jenkins and D. Watts, Spectral analysis and its applications, p.525, 1969.

A. Mangin, Pour Une Meilleure Connaissance Des Systèmes Hydrologiques à Partir Des Analyses Corrélatoire et Spectrale, Journal of Hydrology, vol.67, issue.1-4, pp.25-43, 1984.