Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Mounce, S.R.; Sharpe, R.; Speight, V.; Holden, B.; Boxall, J. (2015)
Languages: English
Types: Other
Stagnation or low turnover of water within water distribution systems may result in water quality issues, even for relatively short durations of stagnation / low turnover if other factors such as deteriorated aging pipe infrastructure are present. As leakage management strategies, including the creation of smaller pressure management zones, are implemented increasingly more dead ends are being created within networks and hence potentially there is an increasing risk to water quality due to stagnation / low turnover. This paper presents results of applying data driven tools to the large corporate databases maintained by UK water companies. These databases include multiple information sources such as asset data, regulatory water quality sampling, customer complaints etc. A range of techniques exist for exploring the interrelationships between various types of variables, with a number of studies successfully using Artificial Neural Networks (ANNs) to probe complex data sets. Self Organising Maps (SOMs), are a class of unsupervised ANN that perform dimensionality reduction of the feature space to yield topologically ordered maps, have been used successfully for similar problems to that posed here. Notably for this application, SOM are trained without classes attached in an unsupervised fashion. Training combines competitive learning (learning the position of a data cloud) and co-operative learning (self-organising of neighbourhoods). Specifically, in this application SOMs performed multidimensional data analysis of a case study area (covering a town for an eight year period). The visual output of the SOM analysis provides a rapid and intuitive means of examining covariance between variables and exploring hypotheses for increased understanding. For example, water age (time from system entry, from hydraulic modelling) in combination with high pipe specific residence time and old cast iron pipe were found to be strong explanatory variables. This derived understanding could ultimately be captured in a tool providing risk based prioritisation scores.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Geldreich, E. E., Microbial quality of water supply in distribution systems . CRC, (1996).
    • [2] EPA, “Distribution system indicators of drinking water quality.” http://www.epa.gov/safewater/disinfection/tcr/index.html , (2006).
    • [3] Carter, J. T., Lee, Y. and Buchberger, S. G.,”Correlation between travel time and water quality in a deadend loop”. Proc.Water Quality Technology Conference. Am. Wat. Wks Assoc., November 9-12, Denver, Co, USA, (1997).
    • [4] Unwin, D., M., Saul A., J. and Boxall J., B., “Data mining and relationship analysis of Water Distribution System Databases for improved understanding of operations performance”. In CCWI Advances in Water Supply Management, London, (2003).
    • [5] Wu, W., Dandy, G. C. and Maier, H. R., “Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling”. Environmental Modelling & Software , Vol. 54, (2014), pp 108-127.
    • [6] Kohonen, T.,”The Self-Organizing Map,” Proceedings of the IEEE, Vol.78, No.9, (1990), pp 1464-1480.
    • [7] Kalteh, A. M., Hjorth, P. and Berndsson, R., “Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application”, Environmental Modelling and Software, Vol. 23, (2008), pp 835-845.
    • [8] Mounce, S. R., Douterelo, I., Sharpe, R. and Boxall, J. B.,“A bio-hydroinformatics application of self-organizing map neural networks for assessing microbial and physicochemical water quality in distribution systems”. Proceedings of 10th International Conference on Hydroinformatics , Hamburg, Germany, (2012).
    • [9] Thompson, R., “A Note on Restricted Maximum Likelihood Estimation with an Alternative Outlier Model”. Journal of the Royal Statistical Society. Series B (Methodological,) Vol. 47, No. 1, pp 53-55, (1985).
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article