The effect of data granularity on prediction of extreme hydrological events in highly urbanized watersheds: A supervised classification approach

During heavy rains, small urbanized watersheds with predominantly impervious surfaces exhibit high surface runoff which may subsequently lead to flash floods. Prediction of such extreme events in an efficient and timely manner is one of the important problems faced by regional flood management teams. These predictions can be done using supervised classification and data collected by stream and rain gauges installed on the watershed. The accuracy of predictions depends on data granularity which determines the achievable level of uncertainty for different lead time intervals. The study was implemented on data collected in a highly urbanized watershed of a small stream - Spring Creek, Ontario, Canada. It was demonstrated that the upscaling of observation data improves the classifiers' performance while increasing modelling scales. The obtained results suggest the development of ensembles of classifiers trained on data sets of different granularity as a means to extend the lead time of reliable predictions.