Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems. (June 2016)

• Record Type: Journal Article
• Authors: Löwe, Roland; Vezzaro, Luca; Mikkelsen, Peter Steen; Grum, Morten; Madsen, Henrik
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 143
• Page End: 158
• Publication Date: 2016-06
• Subjects: Stochastic grey-box model -- Probabilistic forecasting -- Real-time control -- Urban hydrology -- Radar rainfall -- Storm water management; 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.2016.02.027
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This article demonstrates the incorporation of stochastic grey-box models for urban runoff forecasting into a full-scale, system-wide control setup where setpoints are dynamically optimized considering forecast uncertainty and sensitivity of overflow locations in order to reduce combined sewer overflow risk. The stochastic control framework and the performance of the runoff forecasting models are tested in a case study in Copenhagen (76 km 2 with 6 sub-catchments and 7 control points) using 2-h radar rainfall forecasts and inlet flows to control points computed from a variety of noisy/oscillating in-sewer measurements. Radar rainfall forecasts as model inputs yield considerably lower runoff forecast skills than "perfect" gauge-based rainfall observations (ex-post hindcasting). Nevertheless, the stochastic grey-box models clearly outperform benchmark forecast models based on exponential smoothing. Simulations demonstrate notable improvements of the control efficiency when considering forecast information and additionally when considering forecast uncertainty, compared with optimization based on current basin fillings only. Graphical abstract: Highlights: A proof-of-concept for combining probabilistic forecasting and control was provided. Runoff forecasts based on radar rainfall forecasts improved control efficiency. Considering runoff forecast uncertainty in control improved control efficiency. Probabilistic on-line runoff forecasting models were tested for 6 catchments. On-line runoff forecasting models could be implemented despite noisy measurements.