Attributing Minimum Night Flow to Individual Pipes in Real-World Water Distribution Networks Using Machine Learning
Author:
Affiliation:
1. Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK
2. Department of Engineering, University of Exeter, Exeter EX4 4QF, UK
3. South West Water, Exeter EX2 7HR, UK
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/69/1/112/pdf
Reference6 articles.
1. Farley, M., and Trow, S. (2005). Losses in Water Distribution Networks: A Practitioners’ Guide to Assessment, Monitoring and Control, IWA Publishing. [1st ed.].
2. Farrow, J., Jesson, D., Mulheron, M., Nensi, T., and Smith, P. (2017). Achieving Zero Leakage by 2050: Basic Mechanisms of Bursts and Leakage, UK Water Industry Research Limited. [1st ed.].
3. A review of methods for leakage management in pipe networks;Puust;Urban Water J.,2010
4. Applying Minimum Night Flow to Estimate Water Loss Using Statistical Modeling: A Case Study in Kinta Valley, Malaysia;Alkasseh;Water Resour. Manag.,2013
5. Hayslep, M., Keedwell, E., and Farmani, R. (2023, January 15–19). Multi-Objective Multi-Gene Genetic Programming for the Prediction of Leakage in Water Distribution Networks. Proceedings of the Genetic and Evolutionary Computation Conference, Lisbon, Portugal.
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