Simulation of urban wastewater systems using artificial neural networks: Embedding urban areas in integrated catchment modelling

Fu, G; Makropoulos, C; Butler, D

dc.contributor.author	Fu, G	en
dc.contributor.author	Makropoulos, C	en
dc.contributor.author	Butler, D	en
dc.date.accessioned	2014-03-01T01:34:36Z
dc.date.available	2014-03-01T01:34:36Z
dc.date.issued	2010	en
dc.identifier.issn	1464-7141	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20767
dc.subject	Artificial neural network	en
dc.subject	Catchment scale model	en
dc.subject	Integrated catchment management	en
dc.subject	Integrated modelling	en
dc.subject	Urban wastewater system	en
dc.subject	Water quality modelling	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Water Resources	en
dc.subject.other	PART 1	en
dc.subject.other	PERFORMANCE	en
dc.subject.other	METHODOLOGY	en
dc.subject.other	PREDICTION	en
dc.title	Simulation of urban wastewater systems using artificial neural networks: Embedding urban areas in integrated catchment modelling	en
heal.type	journalArticle	en
heal.identifier.primary	10.2166/hydro.2009.151	en
heal.identifier.secondary	http://dx.doi.org/10.2166/hydro.2009.151	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	The urban wastewater system is an important part of integrated water management at the catchment level, yet, more often than not, inclusion of the system and its interaction with the surrounding catchment is either oversimplified or totally ignored in catchment modelling. Reasons of complexity and computational burden are mostly at the heart of this modelling gap. This paper proposes to use artificial neural networks (ANN) as a surrogate for the simulation of the urban wastewater system, allowing for a more realistic representation of the urban component to be incorporated into catchment models within a broad scale modelling framework. As a proof of concept, an integrated urban wastewater model is developed and its response in terms of both quantity and quality in combined sewer overflow (CSO) discharges and treatment plant effluent are captured and used to train a feedforward back-propagation ANN. The comparative results of the integrated urban water model and the ANN show good agreement for both water quantity and quality parameters. The resulting trained network is then embedded into a MIKE BASIN catchment model. It is suggested that ANN models greatly improve the level at which broad scale catchment models can accurately take into account urban-rural interactions. © IWA Publishing 2010 Journal of Hydroinformatics.	en
heal.publisher	I W A PUBLISHING	en
heal.journalName	Journal of Hydroinformatics	en
dc.identifier.doi	10.2166/hydro.2009.151	en
dc.identifier.isi	ISI:000276755000002	en
dc.identifier.volume	12	en
dc.identifier.issue	2	en
dc.identifier.spage	140	en
dc.identifier.epage	149	en