Predictions
in Ungauged Basins: Promise and Progress (Proceedings of symposium S7 held during the
Seventh IAHS Scientific Assembly at Foz do Iguaçu, Brazil, April 2005). IAHS
Publ. 303, 2006, 57-68.
PAI-OFF: a new strategy for online flood
forecasting in mountainous catchments
J. Cullmann, G. H. Schmitz & W. Görner
Institute of Hydrology and Meteorology, Dresden University of
Technology, Würzburger Str 46, D-01187 Dresden, Germany
Johannes.Cullmann@mailbox.tu-dresden.de
Abstract We present PAI-OFF (Process Modelling and Artificial Intelligence for Online Flood Forecasting), which combines the reliability of physically based, sophisticated modelling with the operational advantages of Artificial Neural Networks (ANN). Thus we are able to improve ANN performance in the flood forecasting context by detailed process modelling. Low computation times and robustness are the key features of ANN models and also form the basic requirements for flash flood forecasting. After presenting the theory of the new methodology, the results of a catchment related meteorological analysis for generating storm scenarios serve as the input to a coupled hydrological/hydraulic model, which is set up for a mountainous catchment in east Germany. Along these lines we operate the catchment model for all realistically possible constellations of flood formation. This results in a database consisting of corresponding input/output vectors. We complete the database for training the ANN by adding yet more flood relevant data for characterizing the hydrological and meteorological catchment situation prior to a storm event. After this preparatory step, the ANN is applied for online flash flood forecasting in the considered catchment using an “unseen” storm event, i.e. one which did not feature in the training process. The convincing agreement between the predicted and observed flood hydrograph underlines the application potential of the new PAI-OFF methodology for online flood forecasting even in smaller catchments.
Key words artificial
neural networks; flood forecasting; network training