Urban Hourly Water Demand Prediction Using Human Mobility Data

Smolak, K., Kasieczka, B., Siła-Nowicka, K. , Kopańczyk, K., Rohm, W. and Fiałkiewicz, W. (2019) Urban Hourly Water Demand Prediction Using Human Mobility Data. In: 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2018), Zurich, Switzerland, 17-20 Dec 2018, pp. 213-214. ISBN 9781538655023 (doi: 10.1109/BDCAT.2018.00036)

Abstract

The efficient management of a water supply system requires precise water demand forecasts as inputs. This paper compares existing prediction methods and improves their performance by integrating human-related factors with water consumption in an urban area. Furthermore, a framework for processing and transforming mobility data into time-series is presented. Results show that using human mobility data improves forecasting accuracy reaching 87.6%.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sila-Nowicka, Ms Katarzyna
• Authors: Smolak, K., Kasieczka, B., Siła-Nowicka, K., Kopańczyk, K., Rohm, W., and Fiałkiewicz, W.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• ISBN: 9781538655023
• Copyright Holders: Copyright © 2018 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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