Scientific Results

  • ID:
    publications-4716
  • Type:
    article
  • Year:
    2008
  • Authors:
    Cutore, P. and Cutore, P. and Campisano, Alberto and Campisano, Alberto and Kapelan, Zoran and Kapelan, Zoran and Modica, Carlo and Modica, Carlo and Savić, Dragan and Savic, Dragan
  • Title:
    Probabilistic prediction of urban water consumption using the SCEM-UA algorithm
  • Venue/Journal:
    Urban Water Journal
  • DOI:
    10.1080/15730620701754434
  • Research type:
  • Water System:
  • Technical Focus:
  • Abstract:
    Prediction of urban water consumption can help to improve the performance of water distribution systems. Despite the obvious presence of uncertainty in measurements and in assumed model types/structures, most of the existing water consumption prediction models are developed and used in a deterministic context. Methods for more realistic assessment of parameter and model prediction uncertainties have begun to appear in literature only recently. A novel application of the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) for the calibration of a water consumption prediction model is proposed here. The model is applied to a case study of the city of Catania (Italy) with the aim to predict daily water consumption. The SCEM-UA algorithm is used to calibrate the parameters of the artificial neural network based prediction model and in turn to determine the associated parameter and model prediction uncertainties. The results obtained using the SCEM-UA ANN approach were compared to the corresponding resul...
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