Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks
Chen, Albert S.
International Association of Hydrological Sciences
This paper describes the application of ANNs (Artificial Neural Networks) as DDMs (Data Driven Models) to predict urban flooding in real-time based on BADC weather radar and/or rainfall data. A 123-manhole combined sewer sub-network from Keighley, West Yorkshire, UK is used to demonstrate the methodology. An ANN is configured for prediction of flooding at manholes based on rainfall. In the absence of actual flood data, the 3DNet / SIPSON simulator, which uses a conventional fluid-dynamic approach to predict flooding surcharge levels in sewer networks, is employed to provide the target data for training the ANN. Artificial rainfall profiles derived from observed data provide the input. Both flood-level analogue and flood-severity classification schemes are implemented. We also investigate the use of an ANN for nowcasting of rainfall based on the relationship between radar data and recorded rainfall history. This allows the two ANNs to be cascaded to predict flooding in real-time based on weather radar.
WRAH 2011: Weather Radar and Hydrology International Symposium, 18-21 April 2011, University of Exeter, UK
IAHS Red Book series no. 351
Place of publication