Résumé
In the French Mediterranean regions, heavy rainfall episodes regularly occur and induce very rapid and voluminous floods called flash floods. hey frequently cause fatalities and can cost more than one billion euros during only one event. In order to cope with this issue, the public authorities’ implemented countermeasures in which hydrological forecasting plays an essential role. In this contexte, the French Flood Forecasting Service (called SCHAPI for Service Central d’Hydrométéorologie et d’Appui à la Prévision des Inondations) initiated the BVNE (Digital Experimental Basin, for Bassin Versant Numérique Expérimental) project in order to enhance flash flood forecasts. The present work is a part of this project and aim at three main purposes: providing flash flood forecasts on well-gauged basins, poorly gauged basins and engauged basins. The study area chosen, the Cévennes range, concentrates the major part of these intense hydrometeorological events in France. This dissertation presents it precisely, highlighting its most hydrological-influent characteristics. With regard to the complexity of the rainfall-discharge relation in the focused basins and the difficulty experienced by the physically based models to provide precise information in forecast mode without rainfall forecasts, the use of neural networks statistical learning imposed itself in the research of operational solutions. Thus, the neural networks models were designed and applied to a basin of the Cévennes range, in the well-gauged and poorly gauged contexts. The good results obtained have been the start point of a generalization to 15 basins of the study area. For this purpose, a generalization method was developed from the model created on the gauged basin and from corrections estimated as a function of basin characteristics. The results of this method application are of good quality and open the door to numerous pats of inquiry for the future, while demonstrating again that the use of statistical learning for hydrology can be a relevant solution.