Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Long-term versus Real-time Optimal Operation for Gate Regulation during Flood in Urban Drainage Systems

Authors
Jafari, FatemehMousavi, S. JamshidYazdi, JafarKim, Joong Hoon
Issue Date
2018
Publisher
TAYLOR & FRANCIS LTD
Keywords
Urban drainage systems; flood control; real-time operation; detention reservoirs; long-term operation
Citation
URBAN WATER JOURNAL, v.15, no.8, pp.750 - 759
Indexed
SCIE
SCOPUS
Journal Title
URBAN WATER JOURNAL
Volume
15
Number
8
Start Page
750
End Page
759
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/81043
DOI
10.1080/1573062X.2018.1556307
ISSN
1573-062X
Abstract
In this study, a simulation-optimization framework is built to present two approaches of long-term and real-time optimal operation for the regulation of gates during flood in urban drainage systems (UDSs). The modeling approaches are applied to a prototype network in a portion of the UDS of Tehran, Iran. A sensitivity analysis is conducted to determine the number of decision variables of the optimization models. Results are then compared with those of an uncontrolled (UC) approach as a baseline business-as-usual scenario. It has been inferred that the two optimization approaches outperform the UC method. Additionally, the real-time optimal (RTOP) operation approach, benefiting from both the current system's state and the latest storm data, is superior to the long-term optimal (LTOP) operation approach with respect to flood mitigation efficiency. The RTOP approach has the flexibility to adapt to changes in operational conditions when part of the system's regulation capacity is lost.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > School of Civil, Environmental and Architectural Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE