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• W2792319327 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." @default.
• W2792319327 created "2018-03-29" @default.
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• W2792319327 date "2018-02-02" @default.
• W2792319327 modified "2023-09-27" @default.
• W2792319327 title "Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts" @default.
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• W2792319327 doi "https://doi.org/10.3390/w10020142" @default.
• W2792319327 hasPublicationYear "2018" @default.
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