ID:
publications-2676
Type:
Peer reviewed articles
Year:
2018
Authors:
Sina Shabani, Antonio Candelieri, Francesco Archetti, Gholamreza Naser
Title:
Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts
Venue/Journal:
Water
DOI:
10.3390/w10020142
Research type:
Uncategorized
Water System:
Wastewater Treatment Plants
Technical Focus:
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.
Link with Projects:
690900
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