Shortest path criterion for sampling design of water distribution networks

References

• Behzadian, K., Ardeshir, A., Kapelan, Z.S., and Savic, D.A., 2008. Stochastic sampling design for water distribution model calibration. International Journal of Civil Engineering, 6 (1), 48–57.
   Web of Science ®Google Scholar
• Behzadian, K., Kapelan, Z.S., Savic, D.A., and Ardeshir, A., 2009. Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks. Environmental Modelling & Software, 24, 530–541.
   Web of Science ®Google Scholar
• Bovolin, V. and Villani, P., 2004. Individuazione dei nodi sensibili per il monitoraggio delle reti di distribuzione idrica. In Atti del 29° Convegno di Idraulica e Costruzioni Idrauliche, Trento, Bios Editore. Castrolibero (Cs), Italy: Bios Editore, 149–156 (in Italian).
   Google Scholar
• Bush, C.A. and Uber, J.G., 1998. Sampling design methods for water distribution model calibration. Journal of Water Resources Planning and Management, 124 (6), 334–344.
   Web of Science ®Google Scholar
• de Schaetzen, W.B.F., Walters, G.A., and Savic, D.A., 2000. Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms. Urban Water, 2, 141–152.
   Google Scholar
• Golub, G.H. and Van Loan, C.F., 1996. Matrix computations. Baltimore, MA: The Johns Hopkins University Press.
   Google Scholar
• Del Giudice, G. and Di Cristo, C., 2003. Sampling design for water distribution networks. In: Proceedings of II International Conference on Water Resources Management. April 30-May 2, 2003. Las Palmas, Gran Canaria: WIT-Press, 181–204.
   Google Scholar
• Floyd, R.W., 1962. Algorithm 97: Shortest Path. Communications of the ACM, 5 (6), 345.
   Web of Science ®Google Scholar
• Kapelan, Z.S., Savic, D.A., and Walters, G.A., 2005. Optimal sampling design methodologies for water distribution model calibration. Journal of Hydraulic Engineering, 131 (3), 190–200.
   Web of Science ®Google Scholar
• Kaufman, L. and Rousseeuw, P.J., 1987. Clustering by means of Medoids. Statistical Data Analysis Based on the L₁ Norm and Related Methods, Birkhäuser, Basel, 405–416.
   Google Scholar
• Knopman, D.S. and Voss, C.I., 1989. Multiobjective sampling design for parameter estimation and model discrimination in groundwater solute transport. Water Resources Research, 25 (10), 2245–2258.
   Web of Science ®Google Scholar
• Lansey, K.E., El-Shorbagy, W., Ahmed, I., Araujo, J., and Haan, C.T., 2001. Calibration assessment and data collection for water distribution networks. Journal of Hydraulic Engineering, 127 (4), 270–279.
   Web of Science ®Google Scholar
• Laucelli, D., Berardi, L., and Giustolisi, O., 2011. A proposal of topological sampling design. Computing and Control in Water Industry, CCWI 2011, Exeter, UK, 5–7 September 2011. Centre for Water Systems, University of Exeter, 811–816.
   Google Scholar
• MacQueen, J.B., 1967. Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 281–297.
   Google Scholar
• Meier, R.W. and Barkdoll, B.D., 2000. Sampling design for network model calibration using genetic algorithms. Journal of Water Resources Planning and Management, 126 (4), 245–250.
   Web of Science ®Google Scholar
• Preis, A., Whittle, A., and Ostfeld, A., 2011. Multi-objective optimization for conjunctive placement of hydraulic and water quality sensors in water distribution systems. Water Science & Technology: Water Supply, 11 (2), 166–171.
   Web of Science ®Google Scholar
• Pudar, R.S. and Liggett, J.A., 1992. Leaks in pipe networks. Journal of Hydraulic Engineering, 118 (7), 1031–1046.
   Web of Science ®Google Scholar
• Savic, D.A., Kapelan, Z.S., and Jonkergouw, P.M.R., 2009. Quo vadis water distribution model calibration?Urban Water Journal, 6 (1), 3–22.
   Web of Science ®Google Scholar
• Schrijver, A., 1998. Theory of linear and integer programming. New York: John Wiley and Sons.
   Google Scholar