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.

Shabani, S., Candelieri, A., Archetti, F., Naser, G. (2018). Gene expression programming coupled with unsupervised learning: A two-stage learning process in multi-scale, short-termwater demand forecasts. WATER, 10(2) [10.3390/w10020142].

Gene expression programming coupled with unsupervised learning: A two-stage learning process in multi-scale, short-termwater demand forecasts

Candelieri, A;Archetti, F;
2018

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.
Articolo in rivista - Articolo scientifico
Average mutual information; Clustering; Gene expression programming; Multi-scale modeling; Short-term water demand forecasting;
Average mutual information; Clustering; Gene expression programming; Multi-scale modeling; Short-term water demand forecasting; Biochemistry; Geography, Planning and Development; Aquatic Science; Water Science and Technology
English
2018
10
2
142
open
Shabani, S., Candelieri, A., Archetti, F., Naser, G. (2018). Gene expression programming coupled with unsupervised learning: A two-stage learning process in multi-scale, short-termwater demand forecasts. WATER, 10(2) [10.3390/w10020142].
File in questo prodotto:
File Dimensione Formato  
Gene expression programming coupled with unsupervised learning A two-stage learning process in multi-scale.pdf

accesso aperto

Descrizione: publisher version
Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 1.64 MB
Formato Adobe PDF
1.64 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/204361
Citazioni
  • Scopus 25
  • ???jsp.display-item.citation.isi??? 22
Social impact