Real-Time Water Level Prediction Based on Artificial Neural Networks

Urban flooding is often characterised by short lead times. In combination with the uncertainty in precipitation forecasting, the real-time forecasting of urban flooding is still challenging. Fast physically based models are still too slow for the usage in real-time forecasting. Data driven models are suitable to face this problem. The present study deals with testing an artificial neural network based model for the prediction of water levels with two dimensional spatial distributions at the catchment surface. The model was tested for synthetic rain events in a prior study. In the present study the model is successfully tested for spatially uniform distributed natural rain events.