Urban flood forecast based on raingauge networks

Abstract

For reliable prediction of urban pluvial flooding it is essential to have reliable spatial and temporal rainfall prediction at an appropriate scale. Radar data are considered to be the most reliable. However, many urban catchments do not have access to radar data. This paper presents a new methodology for rainfall forecasting based on a network of raingauges. The methodology predicts rainfall at each raingauge location based on the Support Vector Machine (SVM) technique improved with Singular Spectrum Analysis (SSA). The prediction of the spatial distribution of rainfall is based on interpolation techniques. The forecasted rainfall fields are then used as inputs for simulation of drainage systems to obtain a short-term flood prediction. The proposed methodology is tested using real data from a case study in Coimbra, Portugal and the results obtained showed that it is possible to predict the water level 30 minutes in advance when using this methodology.