An adaptive predictive approach for river level forecasting

Abstract

Efficient flood management requires accurate real time forecasts to allow early warnings, real time control of hydraulics structures or other actions. Commercially available computing tools typically use, for flow or level forecasting, hydraulic models derived from the numerical approximation of Saint-Venant equations. These tools need powerful computers, accurate knowledge of the riverbed topography and skilled operators with some hydraulic background. This paper presents an alternative approach in which the river network is modeled as a cascade of interconnected input-output systems. Each system is modeled by an adaptive predictive expert model, which provides real-time fast and accurate forecasts over a moving prediction horizon. The main advantages of the approach are: (1) simplicity in the formulation and low computational burden; (2) no need of topographic information on the river waterbeds; (3) operators do not need strong hydraulic knowledge and the forecast may be done autonomously. The approach is evaluated using real data from the Ebro river basin in Spain.