Abstract
The feasibility of flash flood forecasting without making use of rainfall predictions is investigated. After a presentation of the “cevenol flash floods“, which caused 1.2 billion Euros of economical damages and 22 fatalities in 2002, the difficulties incurred in the forecasting of such events are analyzed, with emphasis on the nature of the database and the origins of measurement noise. The high level of noise in water level measurements raises a real challenge. For this reason, two regularization methods have been investigated and compared: early stopping and weight decay. It appears that regularization by early stopping provides networks with lower complexity and more accurate predicted hydrographs than regularization by weight decay. Satisfactory results can thus be obtained up to a forecasting horizon of three hours, thereby allowing an early warning of the populations.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
European Flood Forecasting System (2003), http://effs.wldelft.nl/
PREVIEW (2008), http://www.preview-risk.com
Le Lay, M., Saulnier, G.-M.: Exploring the Signature of Climate and Landscape Spatial Variabilities in Flash Flood Events: Case of the 8–9 September 2002 Cévennes-Vivarais Catastrophic Event. Geophysical Research Letters 34, 5 page (2007)
Taramasso, A.C., Gabellani, S., Marsigli, C., Montani, A., Paccagnella, T., Parodi, A.: Operational flash-flood forecasting chain: an application to the Hydroptimet test cases. Geophysical Research Abstracts 7, 9–14 (2005)
Jasper, K., Gurtz, J., Lang, H.: Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model. Journal of Hydrology 267, 40–52 (2002)
CrossGrid (2005), http://www.eu-crossgrid.org
Zealand, C.M., Burn, D.H., Simonovic, S.P.: Short term streamflow forecasting using artificial neural networks. Journal of Hydrology 214, 32–48 (1999)
Schmitz, G.H., Cullmann, J.: PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments. Journal of Hydrology 1, 1–14 (2008)
Iliadis, S.L., Maris, F.: An artificial neural networks model for mountainous water-resources management: the case of Cyprus mountainous watersheds. Environmental Modelling & Software 22, 1066–1072 (2007)
Noilhan, J., Mahfouf, J.F.: The ISBA land surface parameterization scheme. Global and Planetary Change 13, 145–159 (1996)
Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks Are Universal Approximators. Neural Networks 2, 359–366 (1989)
Dreyfus, G.: Neural Networks, Methodology and Applications. Springer, Heidelberg (2005)
Hagan, M.-T., Menhaj, M.-B.: Training feedforward networks with the Marquardt Algorithm. IEEE Transaction on Neural Networks 5(6), 989–993 (1994)
Sjöberg, J., Ljung, L.: Overtraining, regularization, and searching for a minimum, with application to neural networks. International Journal of Control 62(6), 1391–1407 (1995)
Kitadinis, P.K., Bras, R.L.: Real-time forecasting with a conceptual hydrologic model: 2 applications and results. Water Resour. Res. 16, 1034–1044 (1980)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Toukourou, M.S., Johannet, A., Dreyfus, G. (2009). Flash Flood Forecasting by Statistical Learning in the Absence of Rainfall Forecast: A Case Study. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-03969-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03968-3
Online ISBN: 978-3-642-03969-0
eBook Packages: Computer ScienceComputer Science (R0)