| For the modelling of the flood routing in the lower reaches of the
Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic
modelling system HEC-RAS has been applied. Furthermore, this model was used
to generate a database to train multilayer feedforward networks.
To guarantee numerical stability for the hydrodynamic modelling of some 60 km
of streamcourse an adequate resolution in space requires very
small calculation time steps, which are some two orders of magnitude smaller
than the input data resolution. This leads to quite high computation
requirements seriously restricting the application – especially when dealing
with real time operations such as online flood forecasting.
In order to solve this problem we tested the application of Artificial
Neural Networks (ANN). First studies show the ability of adequately trained
multilayer feedforward networks (MLFN) to reproduce the model performance. |