TY - DATA T1 - CCWI2017: F58 'Water Advisory Demand Evaluation and Resource Toolkit' PY - 2017/09/01 AU - Daniel Paluszczyszyn AU - Sunday Iliya AU - Eric Goodyer AU - Tomasz Kubrycht UR - https://orda.shef.ac.uk/articles/journal_contribution/CCWI2017_F58_Water_Advisory_Demand_Evaluation_and_Resource_Toolkit_/5364553 DO - 10.15131/shef.data.5364553.v1 L4 - https://ndownloader.figshare.com/files/9219163 KW - CCWI2017 KW - demand KW - prediction KW - computational intelligence KW - Civil Engineering not elsewhere classified N2 - 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 m 3 in terms of root mean square error. ER -