Abstract
“Cévenol flash floods” are famous in the field of hydrology, because they are archetypical of flash floods that occur in populated areas, thereby causing heavy damages and casualties. As a consequence, their prediction has become a stimulating challenge to designers of mathematical models, whether physics based or machine learning based. Because current, state-of-the-art hydrological models have difficulty performing forecasts in the absence of rainfall previsions, new approaches are necessary. In the present paper, we show that an appropriate model selection methodology, applied to neural network models, provides reliable two-hour ahead flood forecasts.
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Toukourou, M., Johannet, A., Dreyfus, G. et al. Rainfall-runoff modeling of flash floods in the absence of rainfall forecasts: the case of “Cévenol flash floods”. Appl Intell 35, 178–189 (2011). https://doi.org/10.1007/s10489-010-0210-y
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DOI: https://doi.org/10.1007/s10489-010-0210-y