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Water 2018, 10(2), 142; https://doi.org/10.3390/w10020142

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

1
School of Engineering, University of British Columbia, Kelowna, BC V1V 1V7, Canada
2
Department of Computer Science, University of Milano Bicocca, Viale Sarca 336, 20126 Milan, Italy
*
Author to whom correspondence should be addressed.
Received: 21 November 2017 / Revised: 25 January 2018 / Accepted: 30 January 2018 / Published: 2 February 2018
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Abstract

This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand data of the city of Milan, in Italy. Moreover, multi-scale modeling using a series of head-time was deployed to investigate the optimum temporal resolution under study. Multi-scale modeling was performed based on rearranging hourly based patterns of water demand into 3, 6, 12, and 24 h lead times. Results showed that GEP should receive more attention among the emerging nonlinear modelling techniques if coupled with unsupervised learning algorithms in detailed spherical k-means. View Full-Text
Keywords: short-term water demand forecasting; multi-scale modeling; gene expression programming; clustering; average mutual information short-term water demand forecasting; multi-scale modeling; gene expression programming; clustering; average mutual information
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Shabani, S.; Candelieri, A.; Archetti, F.; Naser, G. Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts. Water 2018, 10, 142.

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