| dc.contributor.author | Bailey, J | |
| dc.contributor.author | Harris, E | |
| dc.contributor.author | Keedwell, E | |
| dc.contributor.author | Djordjevic, S | |
| dc.contributor.author | Kapelan, Z | |
| dc.date.accessioned | 2016-11-01T14:56:30Z | |
| dc.date.issued | 2016-08-24 | |
| dc.description.abstract | Issues at Combined Sewer Overflows (CSOs) affect water and sewerage companies’ serviceability performance, can result in environmental impacts through discharges to watercourses, which can incur large costs due to financial penalties for pollution incidents. There is increasing telemetry coverage at CSOs, increasing the availability of data regarding asset performance. This paper presents the application of Artificial Neural Networks (ANNs) to the telemetry data of Dŵr Cymru Welsh Water, alongside RADAR rainfall data, to predict level and provide the identification of issues at CSOs. | en_GB |
| dc.description.sponsorship | The work has been conducted as part of a Knowledge Transfer Partnership (KTP) with funding provided by Innovate UK and Dŵr Cymru Welsh Water (DCWW), working in collaboration with the University of Exeter’s Centre for Water Systems (CWS). | en_GB |
| dc.identifier.citation | Vol. 154, pp. 1201-1208 | en_GB |
| dc.identifier.doi | 10.1016/j.proeng.2016.07.524 | |
| dc.identifier.uri | http://hdl.handle.net/10871/24194 | |
| dc.language.iso | en | en_GB |
| dc.publisher | Elsevier | en_GB |
| dc.rights | This is an open access article under the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). The final version is available from Elsevier via the DOI in this record. | en_GB |
| dc.title | The Use of Telemetry Data for the Identification of Issues at Combined Sewer Overflows | en_GB |
| dc.type | Conference paper | en_GB |
| dc.date.available | 2016-11-01T14:56:30Z | |
| dc.identifier.issn | 1877-7058 | |
| dc.identifier.journal | Procedia Engineering | en_GB |
