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 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 a not insignificant hydraulic background. This paper presents an alternative approach in which the river basin is modeled as a network of cascade 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 approach is evaluated using real data from the Ebro river basin in Spain. The main concluded advantages of the new approach are: (1) the formulation is simple with low computational burden; (2) it does not require topographic information on the river waterbeds; (3) the forecast may be performed autonomously.