Automated Data-Driven Approaches to Evaluating and Interpreting Water Quality Time Series Data from Water Distribution Systems

AbstractWater distribution networks are not inert transport systems. The high-quality water produced at water treatment works is subject to a variety of complex and interacting physical, chemical, and biological interactions within these highly variable, high-surface reactors. In particular, the aging and deteriorating asset condition in water distribution systems can result in a degradation of water quality delivered to the customer, often experienced as discoloration caused by increasing amounts of fine particulate matter. Here, it is proposed that by assessing measured turbidity over time, in particular its correlation with local hydraulics, an assessment of change in risk of fouling can be obtained and asset deterioration inferred. This paper presents a methodology for pairwise monitoring of a hydraulic parameter (flow or pressure) and turbidity using wavelet-based semblance analysis—a novel methodology from another domain, which is applied for the first time to water quality data in distribution syst...

[1]  J B Boxall,et al.  Bacterial water quality and network hydraulic characteristics: a field study of a small, looped water distribution system using culture‐independent molecular methods , 2012, Journal of applied microbiology.

[2]  Joby Boxall,et al.  Modeling Discoloration in Potable Water Distribution Systems , 2005 .

[3]  I. Douterelo,et al.  Influence of hydraulic regimes on bacterial community structure and composition in an experimental drinking water distribution system. , 2013, Water research.

[4]  A J Saul,et al.  Laboratory studies investigating the processes leading to discolouration in water distribution networks. , 2008, Water research.

[5]  Jan Vreeburg.  Discolouration in drinking water systems: a particular approach , 2007 .

[6]  J B Boxall,et al.  Asset deterioration and discolouration in water distribution systems. , 2011, Water research.

[7]  Michael B. Jones,et al.  Analysis of anomaly correlations , 1997 .

[8]  Gordon R. J. Cooper,et al.  Comparing time series using wavelet-based semblance analysis , 2008, Comput. Geosci..

[9]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[10]  J H G Vreeburg,et al.  Discolouration in potable water distribution systems: a review. , 2007, Water research.

[11]  K. Klise,et al.  TESTING WATER QUALITY CHANGE DETECTION ALGORITHMS. , 2008 .

[12]  B. Hansen.  The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity , 2001 .

[13]  A. Haar.  Zur Theorie der orthogonalen Funktionensysteme , 1910 .

[14]  Joby Boxall,et al.  Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows , 2010 .

[15]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[16]  Ilkka T Miettinen,et al.  Removal of soft deposits from the distribution system improves the drinking water quality. , 2004, Water research.

[17]  Joby Boxall,et al.  Modelling both the continual erosion and regeneration of discolouration material in drinking water distribution systems , 2014 .

[18]  Joby Boxall,et al.  Discoloration Material Accumulation in Water Distribution Systems , 2011 .

[19]  Stephen R. Mounce,et al.  Linking distribution system water quality issues to possible causes via hydraulic pathways , 2013, Environ. Model. Softw..

[20]  John Machell,et al.  Water quality event detection and customer complaint clustering analysis in distribution systems , 2012 .

[21]  Ronald W. Masulis,et al.  Correlations in Price Changes and Volatility Across International Stock Markets , 1990 .

[22]  John W. Gaffney,et al.  Need for and Use of High-Resolution Turbidity Monitoring in Managing Discoloration in Distribution , 2012 .

[23]  Dominic Cook.  Field investigation of discolouration material accumulation rates in live drinking Water distribution systems. , 2007 .