The effect of data granularity on prediction of extreme hydrological events in highly urbanized watersheds: A supervised classification approach. (October 2017)

• Record Type: Journal Article
• Authors: Erechtchoukova, Marina G.; Khaiter, Peter A.
• Is Part Of: Environmental modelling & software. Volume 96(2017)
• Page Start: 232
• Page End: 238
• Publication Date: 2017-10
• Subjects: Hydrological scale -- Data granularity -- Extreme event -- Hydrological prediction -- Supervised classification -- Time delay embedding; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.06.037
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Granularity of data from 15 min to one hour on high-flow event predictions is investigated Events are predicted by ensembles of classifiers developed using multiple inducers Ensembles are trained on reconstructed phase spaces based on the time-delay embedding Data aggregation improves the predictor performance and increases modelling scale Improvement can be achieved by integrating results of predictors of different modelling scales