Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems

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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 km2 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.
Original languageEnglish
JournalEnvironmental Modelling & Software
Volume80
Pages (from-to)143-158
ISSN1364-8152
DOIs
Publication statusPublished - 2016

Keywords

  • Stochastic grey-box model
  • Probabilistic forecasting
  • Real-time control
  • Urban hydrology
  • Radar rainfall
  • Storm water management

Cite this

@article{bf5ee47cd9154fc4888034b096eb79cb,
title = "Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems",
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 km2 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.",
keywords = "Stochastic grey-box model, Probabilistic forecasting, Real-time control, Urban hydrology, Radar rainfall, Storm water management",
author = "Roland L{\"o}we and Luca Vezzaro and Mikkelsen, {Peter Steen} and Morten Grum and Henrik Madsen",
year = "2016",
doi = "10.1016/j.envsoft.2016.02.027",
language = "English",
volume = "80",
pages = "143--158",
journal = "Environmental Modelling & Software",
issn = "1364-8152",
publisher = "Pergamon Press",

}

Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems. / Löwe, Roland; Vezzaro, Luca; Mikkelsen, Peter Steen; Grum, Morten; Madsen, Henrik.

In: Environmental Modelling & Software, Vol. 80, 2016, p. 143-158.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems

AU - Löwe, Roland

AU - Vezzaro, Luca

AU - Mikkelsen, Peter Steen

AU - Grum, Morten

AU - Madsen, Henrik

PY - 2016

Y1 - 2016

N2 - 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 km2 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.

AB - 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 km2 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.

KW - Stochastic grey-box model

KW - Probabilistic forecasting

KW - Real-time control

KW - Urban hydrology

KW - Radar rainfall

KW - Storm water management

U2 - 10.1016/j.envsoft.2016.02.027

DO - 10.1016/j.envsoft.2016.02.027

M3 - Journal article

VL - 80

SP - 143

EP - 158

JO - Environmental Modelling & Software

JF - Environmental Modelling & Software

SN - 1364-8152

ER -