Articles | Volume 10, issue 2
https://doi.org/10.5194/dwes-10-75-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.Special issue:
Modeling and clustering water demand patterns from real-world smart meter data
Related subject area
Tools: Modeling and simulation
Algorithms for optimization of branching gravity-driven water networks
The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies
Limitations of demand- and pressure-driven modeling for large deficient networks
Identifying (subsurface) anthropogenic heat sources that influence temperature in the drinking water distribution system
Drink. Water Eng. Sci., 11, 67–85,
2018Drink. Water Eng. Sci., 11, 49–65,
2018Drink. Water Eng. Sci., 10, 93–98,
2017Drink. Water Eng. Sci., 10, 83–91,
2017Cited articles
Adamowski, J. F.: Peak daily water demand forecast modeling using artificial neural networks, J. Water Res. Pl.-ASCE, 134, 119–128, 2008.
Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, 1974.
Aksela, K. and Aksela, M.: Demand estimation with automated meter reading in a distribution network, J. Water Res. Pl.-ASCE, 137, 456–467, 2010.
Blokker, E., Vreeburg, J., and Van Dijk, J.: Simulating residential water demand with a stochastic end-use model, J. Water Res. Pl.-ASCE, 136, 19–26, 2009.
Box, G. E. and Cox, D. R.: An analysis of transformations, J. Roy. Stat. Soc. B, 211–252, 1964.