Multi-objective rehabilitation of urban drainage systems under uncertainties

Urban drainage systems are subject to many drivers which can affect their performance and functioning. Typically, climate change, urbanisation and population growth along with aging of pipes may lead to uncontrollable discharges and surface flooding. So far, many researchers and practitioners concer...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of hydroinformatics 2014-01, Vol.16 (5), p.1044-1061
Hauptverfasser: VOJINOVIC, Z, SAHLU, S, TORRES, A. S, SEYOUM, S. D, ANVARIFAR, F, MATUNGULU, H, BARRETO, W, SAVIC, D, KAPELAN, Z
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Urban drainage systems are subject to many drivers which can affect their performance and functioning. Typically, climate change, urbanisation and population growth along with aging of pipes may lead to uncontrollable discharges and surface flooding. So far, many researchers and practitioners concerned with optimal design and rehabilitation of urban drainage systems have applied deterministic approaches which treat input parameters as fixed values. However, due to the variety of uncertainties associated with input parameters, such approaches can easily lead to either over-dimensioning or under-dimensioning of drainage networks. The present paper deals with such issues and describes a methodology that has been developed to accommodate the effects of uncertainties into the design and rehabilitation of drainage systems. The paper presents a methodology that can take into account uncertainties from climate change, urbanisation, population growth and aging of pipes. The methodology is applied and tested on a case study of Dhaka, Bangladesh. The urban drainage network optimisation problem is posed as a multi-objective problem for which the objective functions are formulated to minimise damage costs and intervention costs. Two approaches were evaluated and the results show that both approaches are capable of identifying optimal Pareto fronts.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2014.223