@article{WOS:000363400300008,
abstract = {In this work, identification of 24-hours-ahead water demand prediction
model based on historical water demand data is considered. As part of
the identification procedure, the input variable selection algorithm
based on partial mutual information is implemented. It is shown that
meteorological data on a daily basis are not relevant for the water
demand prediction in the sense of partial mutual information for the
analysed water distribution systems of the cities of Tavira, Algarve,
Portugal and Evanton East, Scotland, UK. Water demand prediction system
is modelled using artificial neural networks, which offer a great
potential for the identification of complex dynamic systems. The
adaptive tuning procedure of model parameters is also developed in order
to enable the model to adapt to changes in the system. A significant
improvement of the prediction ability of such a model in relation to the
model with fixed parameters is shown when a certain trend is present in
the water demand profile.},
address = {ALLIANCE HOUSE, 12 CAXTON ST, LONDON SW1H0QS, ENGLAND},
author = {Banjac, G and Vasak, M and Baotic, M},
doi = {10.2166/ws.2015.048},
issn = {1606-9749},
journal = {WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY},
keywords = {artificial neural networks; online parameters tuni},
number = {5},
pages = {958--964},
publisher = {IWA PUBLISHING},
title = {{Adaptable urban water demand prediction system}},
type = {Article},
volume = {15},
year = {2015}
}

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