Water Advisory Demand Evaluation and Resource Toolkit
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Iliya, S. | en |
| dc.contributor.author | Paluszczyszyn, D. | en |
| dc.contributor.author | Goodyer, E. | en |
| dc.contributor.author | Kubrycht, T. | en |
| dc.date.acceptance | 2017-07-24 | en |
| dc.date.accessioned | 2017-09-06T13:09:11Z | |
| dc.date.available | 2017-09-06T13:09:11Z | |
| dc.date.issued | 2017-09-04 | |
| dc.description.abstract | The purpose of this feasibility study is to determine if the application of computational intelligence can be used to analyse the apparently unrelated data sources (social media, grid usage, traffic/transportation and weather) to produce credible predictions for water demand. For this purpose the artificial neural networks were employed to demonstrate on datasets localised to Leicester city in United Kingdom that viable predictions can be obtained with use of data derived from the expanding Internet-of-Things ecosystem. The outcomes from the initial study are promising as the water demand can be predicted with accuracy of 0.346 m3 in terms of root mean square error. | en |
| dc.funder | Innovate UK | en |
| dc.identifier.citation | Paluszczyszyn, D., Iliya, S., Goodyer, E. and Kubrycht, T. (2017) Water Advisory Demand Evaluation and Resource Toolkit. CCWI2017, 2017 | en |
| dc.identifier.doi | https://doi.org/10.15131/shef.data.5364553.v1 | |
| dc.identifier.uri | http://hdl.handle.net/2086/14467 | |
| dc.language.iso | en | en |
| dc.peerreviewed | No | en |
| dc.projectid | WADER | en |
| dc.researchgroup | DIGITS | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | water demand | en |
| dc.subject | prediction | en |
| dc.subject | computational intelligence | en |
| dc.title | Water Advisory Demand Evaluation and Resource Toolkit | en |
| dc.type | Conference | en |