Parameter identification using optimization techniques in open-channel inverse problems

Adverse socio-economic impacts of recent floods both in Europe and other continents emphasize the need for accurate flood forecasting capabilities towards improved flood risk management services. Flood forecasting models are often data-intensive. These models are inherited with (i) conceptual parameters that often cannot be assessed by field measurements, as in conceptual models; and/or (ii) empirical parameters that their direct measurements are either difficult, for example, roughness coefficient or costly, for example, survey data. There is also a category of practical problems, where modelling is required but gauged data are not available. Models, other than purely theoretical ones, for example, Large Eddy Simulation models, need calibration and the problem is even more pronounced in the case of ungauged rivers. Optimal values of these parameters in a mathematical sense can be identified by a number of techniques as discussed and applied in this paper. New generations of satellites are now able to provide observation data that can be useful to implement these techniques. This paper presents the results of synthesized flood data emulating data obtained from remote sensing. A one-dimensional, steady-state flow in a channel of simple geometry is studied. The paper uses optimization methods and the Extended Kalman Filter to ascertain/improve the values of the parameters.