Flash Flood Forecasting by Statistical Learning in the Absence of Rainfall Forecast: A Case Study. Engineering Applications of Neural Networks

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