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Dr. Stephen Mounce
Department of Civil and Structural Engineering, University of Sheffield, Sir Frederick Mappin Building, Mappin Street, Sheffield, S1 3JD, UK

Basic Info

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Research Keywords & Expertise

0 Data-driven modelling
0 Leakage (including smart meters/networks)
0 CSO analytics
0 Water quality and burst event detection systems
0 Fuzzy RTC

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Water quality and burst event detection systems
Data-driven modelling

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Chapter
Published: 22 June 2020 in The Handbook of Environmental Chemistry
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We are witnessing an industry change which is transitioning to a more intelligent (or smarter) water network. In the UK a 5-year planning period and investment cycle called the Asset Management Plan (AMP) is the regulatory mechanism. This process is used to manage a water utility’s infrastructure and other assets to deliver an agreed standard of service. The challenge of AMP 6 and 7 (to 2025) and beyond is to maximise efficiency by moving from reactive to proactive management. This can be achieved by using data, information and (where possible) control of the system. The more intelligence that is captured, the more that can be learned and understood about the network and subsequently be predicted. Extra data provides new opportunities for asset maintenance and event analytics. Data science is an emerging discipline which combines analysis, programming and business knowledge and uses new and advanced techniques and technologies to work with complex data. The water sector needs to address the issue of ā€˜big data’ and obtaining ā€˜signal from the noise’. Primarily, the focus is on data to action by the application of data science. The role of digitalisation for smart water networks is covered in this chapter, exploring issues of IoT, artificial intelligence, blockchain and other novel technologies. Reference to case studies demonstrates the type of applications which will become increasingly common place. Some recommendations based on future possibilities and opportunities are proposed.

ACS Style

Stephen R. Mounce. Data Science Trends and Opportunities for Smart Water Utilities. The Handbook of Environmental Chemistry 2020, 1 -26.

AMA Style

Stephen R. Mounce. Data Science Trends and Opportunities for Smart Water Utilities. The Handbook of Environmental Chemistry. 2020; ():1-26.

Chicago/Turabian Style

Stephen R. Mounce. 2020. "Data Science Trends and Opportunities for Smart Water Utilities." The Handbook of Environmental Chemistry , no. : 1-26.

Journal article
Published: 24 December 2019 in Journal of Hydroinformatics
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Urban flooding damages properties, causes economic losses and can seriously threaten public health. An innovative, fuzzy logic (FL)-based, local autonomous real-time control (RTC) approach for mitigating this hazard utilising the existing spare capacity in urban drainage networks has been developed. The default parameters for the control algorithm, which uses water level-based data, were derived based on domain expert knowledge and optimised by linking the control algorithm programmatically to a hydrodynamic sewer network model. This paper describes a novel genetic algorithm (GA) optimisation of the FL membership functions (MFs) for the developed control algorithm. In order to provide the GA with strong training and test scenarios, the compiled rainfall time series based on recorded rainfall and incorporating multiple events were used in the optimisation. Both decimal and integer GA optimisations were carried out. The integer optimisation was shown to perform better on unseen events than the decimal version with considerably reduced computational run time. The optimised FL MFs result in an average 25% decrease in the flood volume compared to those selected by experts for unseen rainfall events. This distributed, autonomous control using GA optimisation offers significant benefits over traditional RTC approaches for flood risk management.

ACS Style

S. R. Mounce; Will Shepherd; S. Ostojin; Mohamad Abdel-Aal; A. N. A. Schellart; J. D. Shucksmith; S. J. Tait. Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms. Journal of Hydroinformatics 2019, 22, 281 -295.

AMA Style

S. R. Mounce, Will Shepherd, S. Ostojin, Mohamad Abdel-Aal, A. N. A. Schellart, J. D. Shucksmith, S. J. Tait. Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms. Journal of Hydroinformatics. 2019; 22 (2):281-295.

Chicago/Turabian Style

S. R. Mounce; Will Shepherd; S. Ostojin; Mohamad Abdel-Aal; A. N. A. Schellart; J. D. Shucksmith; S. J. Tait. 2019. "Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms." Journal of Hydroinformatics 22, no. 2: 281-295.

Journal article
Published: 14 March 2019 in Smart Water
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A discolouration concept is proposed describing simultaneous pipe wall material accumulation and mobilisation processes that define discolouration in drinking water distribution systems, one of the biggest causes of customer dissatisfaction. Validation of these mathematical forms is presented. The model formulation was shown to maintain the mobilisation functionality of previous validated shear-stress-dependant modelling tool, but requiring only two empirical parameters. Two distinct operational datasets are then analysed and robust empirical model parameter calibration is obtained utilising a refined particle swarm optimisation technique. The model is shown to make usefully accurate simulations for flow mediated events, providing evidence of predictive capabilities. The combined tracking of accumulation and mobilisation behaviour enables assessment of the current and future discolouration risk posed by any pipe irrespective of age or material, allowing pro-active, risk based planning and prioritisation of maintenance interventions to protect the quality of delivered water.

ACS Style

William R. Furnass; Stephen R. Mounce; Stewart Husband; Richard P. Collins; Joby Boxall. Calibrating and validating a combined accumulation and mobilisation model for water distribution system discolouration using particle swarm optimisation. Smart Water 2019, 4, 3 .

AMA Style

William R. Furnass, Stephen R. Mounce, Stewart Husband, Richard P. Collins, Joby Boxall. Calibrating and validating a combined accumulation and mobilisation model for water distribution system discolouration using particle swarm optimisation. Smart Water. 2019; 4 (1):3.

Chicago/Turabian Style

William R. Furnass; Stephen R. Mounce; Stewart Husband; Richard P. Collins; Joby Boxall. 2019. "Calibrating and validating a combined accumulation and mobilisation model for water distribution system discolouration using particle swarm optimisation." Smart Water 4, no. 1: 3.

Journals
Published: 27 February 2019 in Environmental Science: Water Research & Technology
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Understanding the processes and interactions occurring within complex, ageing drinking water distribution systems is vital to ensuring the supply of safe drinking water.

ACS Style

Vanessa L. Speight; Stephen R. Mounce; Joseph B. Boxall. Identification of the causes of drinking water discolouration from machine learning analysis of historical datasets. Environmental Science: Water Research & Technology 2019, 5, 747 -755.

AMA Style

Vanessa L. Speight, Stephen R. Mounce, Joseph B. Boxall. Identification of the causes of drinking water discolouration from machine learning analysis of historical datasets. Environmental Science: Water Research & Technology. 2019; 5 (4):747-755.

Chicago/Turabian Style

Vanessa L. Speight; Stephen R. Mounce; Joseph B. Boxall. 2019. "Identification of the causes of drinking water discolouration from machine learning analysis of historical datasets." Environmental Science: Water Research & Technology 5, no. 4: 747-755.

Proceedings article
Published: 20 September 2018
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A nonlinear autoregressive exogenous artificial neural network model was developed to predict turbidity response in two different trunk mains with measured flow and turbidity data. Models were initially established to prepare the data and automatically select the appropriate events for model training. Then, an autoregressive exogenous network model was developed and applied to predict turbidity responses based on past events in the time series. A per site continual data driven calculation of turbidity event risk was included as an additional input to capture the effect of temporal distance between the selected events as well as increasing the accuracy of the predictions. The calculated normalised mean square error and mean absolute error showed that the developed model combined with the data preparation and pre- processing models provides good regressions on a future event with a period of 7 to 10 hours for a multi-step ahead prediction. Furthermore, the result of the autoregressive exogenous network was compared with the output of a feed-forward network where the former significantly outperformed the latter (R value of approximately 0.97 compared to 0.66).

ACS Style

Ehsan Kazemi; Stephen Mounce; Stewart Husband; Joby Boxall. Predicting Turbidity in Water Distribution Trunk Mains Using Nonlinear Autoregressive Exogenous Artificial Neural Networks. 2018, 1 .

AMA Style

Ehsan Kazemi, Stephen Mounce, Stewart Husband, Joby Boxall. Predicting Turbidity in Water Distribution Trunk Mains Using Nonlinear Autoregressive Exogenous Artificial Neural Networks. . 2018; ():1.

Chicago/Turabian Style

Ehsan Kazemi; Stephen Mounce; Stewart Husband; Joby Boxall. 2018. "Predicting Turbidity in Water Distribution Trunk Mains Using Nonlinear Autoregressive Exogenous Artificial Neural Networks." , no. : 1.

Conference paper
Published: 20 September 2018
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Water loss from water distribution systems (WDS) is an ongoing problem which poses a significant risk to water resources around the world. This paper presents a novel combined sensor placement – leak/burst localisation methodology which forms, and analyses by using sc inverse-distance weighted (IDW) interpolation, a sensitivity matrix to determine, on average, how accurately each sensor configuration localises leaks/bursts modelled at all nodes in a WDS. For a given number of sensors, the multi-objective evolutionary algorithm determines the optimal location of sensors to maximise the leak/burst localisation performance using the sc-IDW outputs in its objective function. Once the optimal sensor location is selected, the sc-IDW technique is used when new leaks/bursts occur in the WDS to determine their approximate location. A benchmark WDS was used to compare the leak/burst localisation performance against a baseline sensor placement technique. The comparison indicated that by using the sc-IDW technique for both the sensor placement and leak/burst localisation steps the leak/burst search area was reduced in size by between 9 and 26%. Reducing the leak/burst search area allows field teams to more quickly repair a leak/burst and reduce the impact that it has on water company operational efficiency and customer service.

ACS Style

Shaun Boatwright; Michele Romano; Stephen Mounce; Kevin Woodward; Joby Boxall. Optimal Sensor Placement and Leak/Burst Localisation in a Water Distribution System Using Spatially-Constrained Inverse-Distance Weighted Interpolation. 2018, 1 .

AMA Style

Shaun Boatwright, Michele Romano, Stephen Mounce, Kevin Woodward, Joby Boxall. Optimal Sensor Placement and Leak/Burst Localisation in a Water Distribution System Using Spatially-Constrained Inverse-Distance Weighted Interpolation. . 2018; ():1.

Chicago/Turabian Style

Shaun Boatwright; Michele Romano; Stephen Mounce; Kevin Woodward; Joby Boxall. 2018. "Optimal Sensor Placement and Leak/Burst Localisation in a Water Distribution System Using Spatially-Constrained Inverse-Distance Weighted Interpolation." , no. : 1.

Article
Published: 09 March 2017 in Water Resources Management
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Safe, trusted drinking water is fundamental to society. Discolouration is a key aesthetic indicator visible to customers. Investigations to understand discolouration and iron failures in water supply systems require assessment of large quantities of disparate, inconsistent, multidimensional data from multiple corporate systems. A comprehensive data matrix was assembled for a seven year period across the whole of a UK water company (serving three million people). From this a novel data driven tool for assessment of iron risk was developed based on a yearly update and ranking procedure, for a subset of the best quality data. To avoid a ā€˜black box’ output, and provide an element of explanatory (human readable) interpretation, classification decision trees were utilised. Due to the very limited number of iron failures, results from many weak learners were melded into one high-quality ensemble predictor using the RUSBoost algorithm which is designed for class imbalance. Results, exploring simplicity vs predictive power, indicate enough discrimination between variable relationships in the matrix to produce ensemble decision tree classification models with good accuracy for iron failure estimation at District Management Area (DMA) scale. Two model variants were explored: ā€˜Nowcast’ (situation at end of calendar year) and ā€˜Futurecast’ (predict end of next year situation from this year’s data). The Nowcast 2014 model achieved 100% True Positive Rate (TPR) and 95.3% True Negative Rate (TNR), with 3.3% of DMAs classified High Risk for un-sampled instances. The Futurecast 2014 achieved 60.5% TPR and 75.9% TNR, with 25.7% of DMAs classified High Risk for un-sampled instances. The output can be used to focus preventive measures to improve iron compliance.

ACS Style

S. R. Mounce; K. Ellis; J. M. Edwards; V. L. Speight; N. Jakomis; Joby Boxall. Ensemble Decision Tree Models Using RUSBoost for Estimating Risk of Iron Failure in Drinking Water Distribution Systems. Water Resources Management 2017, 31, 1575 -1589.

AMA Style

S. R. Mounce, K. Ellis, J. M. Edwards, V. L. Speight, N. Jakomis, Joby Boxall. Ensemble Decision Tree Models Using RUSBoost for Estimating Risk of Iron Failure in Drinking Water Distribution Systems. Water Resources Management. 2017; 31 (5):1575-1589.

Chicago/Turabian Style

S. R. Mounce; K. Ellis; J. M. Edwards; V. L. Speight; N. Jakomis; Joby Boxall. 2017. "Ensemble Decision Tree Models Using RUSBoost for Estimating Risk of Iron Failure in Drinking Water Distribution Systems." Water Resources Management 31, no. 5: 1575-1589.

Journal article
Published: 20 April 2016 in Environments
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Understanding and managing water quality in drinking water distribution system is essential for public health and wellbeing, but is challenging due to the number and complexity of interacting physical, chemical and biological processes occurring within vast, deteriorating pipe networks. In this paper we explore the application of Self Organising Map techniques to derive such understanding from international data sets, demonstrating how multivariate, non-linear techniques can be used to identify relationships that are not discernible using univariate and/or linear analysis methods for drinking water quality. The paper reports on how various microbial parameters correlated with modelled water ages and were influenced by water temperatures in three drinking water distribution systems.

ACS Style

E.J. Mirjam Blokker; William R. Furnass; John Machell; Stephen R. Mounce; Peter G. Schaap; Joby B. Boxall. Relating Water Quality and Age in Drinking Water Distribution Systems Using Self-Organising Maps. Environments 2016, 3, 10 .

AMA Style

E.J. Mirjam Blokker, William R. Furnass, John Machell, Stephen R. Mounce, Peter G. Schaap, Joby B. Boxall. Relating Water Quality and Age in Drinking Water Distribution Systems Using Self-Organising Maps. Environments. 2016; 3 (4):10.

Chicago/Turabian Style

E.J. Mirjam Blokker; William R. Furnass; John Machell; Stephen R. Mounce; Peter G. Schaap; Joby B. Boxall. 2016. "Relating Water Quality and Age in Drinking Water Distribution Systems Using Self-Organising Maps." Environments 3, no. 4: 10.

Journal article
Published: 01 November 2015 in Journal of Water Resources Planning and Management
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Water 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 systems. It is suggested and subsequently explored through case studies that an increasing (anti) correlation of the turbidity with the (pressure) flow diurnal cycle will be indicative of increasing fouling risk. This can be further supported through evaluation of the rate and magnitude of drift and through assessment of the change in magnitude of the daily turbidity profile. The composite of these approaches is applied to an extensive data set from a United Kingdom distribution system revealing the effectiveness of the analysis preflushing and postflushing (reducing discoloration events by 64–89%). With increasing proliferation of monitoring devices and real-time data acquisition the potential for online systems and well-informed proactive management is apparent.

ACS Style

S. R. Mounce; J. W. Gaffney; S. Boult; J. B. Boxall. Automated Data-Driven Approaches to Evaluating and Interpreting Water Quality Time Series Data from Water Distribution Systems. Journal of Water Resources Planning and Management 2015, 141, 04015026 .

AMA Style

S. R. Mounce, J. W. Gaffney, S. Boult, J. B. Boxall. Automated Data-Driven Approaches to Evaluating and Interpreting Water Quality Time Series Data from Water Distribution Systems. Journal of Water Resources Planning and Management. 2015; 141 (11):04015026.

Chicago/Turabian Style

S. R. Mounce; J. W. Gaffney; S. Boult; J. B. Boxall. 2015. "Automated Data-Driven Approaches to Evaluating and Interpreting Water Quality Time Series Data from Water Distribution Systems." Journal of Water Resources Planning and Management 141, no. 11: 04015026.

Journal article
Published: 01 September 2015 in Procedia Engineering
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Variations in spot-sampled and continuously-monitored water quality data were assessed to determine whether they could be linked to regulatory coliform failures. Data were available from raw water to the final monitoring point at water treatment works (WTW)-B and included climate, physico-chemical and bacteriological data. These were analysed using cross-correlation and self-organising maps in MATLABĀ®. The results highlighted rainfall and upstream coliforms and turbidity as important factors in the coliform failures. Further examination showed that failures correlated with low turbidity and low coliform loading, but relatively high rainfall. This outcome could be used to improve bacteriological compliance at WTW-B and similar sites.

ACS Style

K. Ellis; Stephen Mounce; B. Ryan; C.A. Biggs; M.R. Templeton. Improving Root Cause Analysis of Bacteriological Water Quality Failures at Water Treatment Works. Procedia Engineering 2015, 119, 309 -318.

AMA Style

K. Ellis, Stephen Mounce, B. Ryan, C.A. Biggs, M.R. Templeton. Improving Root Cause Analysis of Bacteriological Water Quality Failures at Water Treatment Works. Procedia Engineering. 2015; 119 ():309-318.

Chicago/Turabian Style

K. Ellis; Stephen Mounce; B. Ryan; C.A. Biggs; M.R. Templeton. 2015. "Improving Root Cause Analysis of Bacteriological Water Quality Failures at Water Treatment Works." Procedia Engineering 119, no. : 309-318.

Journal article
Published: 07 April 2015 in Urban Water Journal
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ACS Style

S.R. Mounce; R.B. Mounce; J.B. Boxall. Case-based reasoning to support decision making for managing drinking water quality events in distribution systems. Urban Water Journal 2015, 13, 727 -738.

AMA Style

S.R. Mounce, R.B. Mounce, J.B. Boxall. Case-based reasoning to support decision making for managing drinking water quality events in distribution systems. Urban Water Journal. 2015; 13 (7):727-738.

Chicago/Turabian Style

S.R. Mounce; R.B. Mounce; J.B. Boxall. 2015. "Case-based reasoning to support decision making for managing drinking water quality events in distribution systems." Urban Water Journal 13, no. 7: 727-738.

Journal article
Published: 21 January 2015 in Journal of Hydroinformatics
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Particulate material accumulates over time as cohesive layers on internal pipeline surfaces in water distribution systems (WDS). When mobilised, this material can cause discolouration. This paper explores factors expected to be involved in this accumulation process. Two complementary machine learning methodologies are applied to significant amounts of real world field data from both a qualitative and a quantitative perspective. First, Kohonen self-organising maps were used for integrative and interpretative multivariate data mining of potential factors affecting accumulation. Second, evolutionary polynomial regression (EPR), a hybrid data-driven technique, was applied that combines genetic algorithms with numerical regression for developing easily interpretable mathematical model expressions. EPR was used to explore producing novel simple expressions to highlight important accumulation factors. Three case studies are presented: UK national and two Dutch local studies. The results highlight bulk water iron concentration, pipe material and looped network areas as key descriptive parameters for the UK study. At the local level, a significantly increased third data set allowed K-fold cross validation. The mean cross validation coefficient of determination was 0.945 for training data and 0.930 for testing data for an equation utilising amount of material mobilised and soil temperature for estimating daily regeneration rate. The approach shows promise for developing transferable expressions usable for pro-active WDS management.

ACS Style

S. R. Mounce; E. J. M. Blokker; S. P. Husband; W. R. Furnass; P. G. Schaap; J. B. Boxall. Multivariate data mining for estimating the rate of discolouration material accumulation in drinking water distribution systems. Journal of Hydroinformatics 2015, 18, 96 -114.

AMA Style

S. R. Mounce, E. J. M. Blokker, S. P. Husband, W. R. Furnass, P. G. Schaap, J. B. Boxall. Multivariate data mining for estimating the rate of discolouration material accumulation in drinking water distribution systems. Journal of Hydroinformatics. 2015; 18 (1):96-114.

Chicago/Turabian Style

S. R. Mounce; E. J. M. Blokker; S. P. Husband; W. R. Furnass; P. G. Schaap; J. B. Boxall. 2015. "Multivariate data mining for estimating the rate of discolouration material accumulation in drinking water distribution systems." Journal of Hydroinformatics 18, no. 1: 96-114.

Journal article
Published: 01 January 2015 in Procedia Engineering
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One-third of utilities around the globe report a loss of more than 40 percent of clean water due to leaks. By reducing the amount of water leaked, smart water networks can help reduce the money wasted on producing or purchasing water, and the related energy required to pump water and treat water for distribution. A UK demo site is presented focusing on leak management, integrating fixed flow and pressure instrumentation, advanced (smart) metering infrastructure and novel instruments (capable of high resolution monitoring). Example data analysis results for this site using the AURA-Alert anomaly detection system for Condition Monitoring are presented

ACS Style

Stephen Mounce; Carlos Pedraza; T. Jackson; P. Linford; Joby Boxall. Cloud Based Machine Learning Approaches for Leakage Assessment and Management in Smart Water Networks. Procedia Engineering 2015, 119, 43 -52.

AMA Style

Stephen Mounce, Carlos Pedraza, T. Jackson, P. Linford, Joby Boxall. Cloud Based Machine Learning Approaches for Leakage Assessment and Management in Smart Water Networks. Procedia Engineering. 2015; 119 ():43-52.

Chicago/Turabian Style

Stephen Mounce; Carlos Pedraza; T. Jackson; P. Linford; Joby Boxall. 2015. "Cloud Based Machine Learning Approaches for Leakage Assessment and Management in Smart Water Networks." Procedia Engineering 119, no. : 43-52.

Journal article
Published: 01 January 2015 in Procedia Engineering
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ACS Style

Kate Ellis; Stephen R. Mounce; Jonathan Edwards; Vanessa Speight; Natalie Jakomis; Joby Boxall. Interpreting and Estimating the Risk of Iron Failures. Procedia Engineering 2015, 119, 299 -308.

AMA Style

Kate Ellis, Stephen R. Mounce, Jonathan Edwards, Vanessa Speight, Natalie Jakomis, Joby Boxall. Interpreting and Estimating the Risk of Iron Failures. Procedia Engineering. 2015; 119 ():299-308.

Chicago/Turabian Style

Kate Ellis; Stephen R. Mounce; Jonathan Edwards; Vanessa Speight; Natalie Jakomis; Joby Boxall. 2015. "Interpreting and Estimating the Risk of Iron Failures." Procedia Engineering 119, no. : 299-308.

Journal article
Published: 17 December 2014 in Procedia Engineering
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It has been demonstrated that particulate material accumulates as cohesive layers on pipeline surfaces in drinking water distribution systems (WDS) and that when mobilized this material can cause discoloration and other water quality issues. This paper investigates the factors involved in this accumulation rate from real world field data. A data-driven modelling approach is adopted, whereby two machine-learning methods are applied for multivariate data mining based on the observed phenomena. The results highlight bulk water iron concentration, pipe material and looped network areas as key descriptive parameters. Such understanding and expressions are important for pro-active network management.

ACS Style

S.R. Mounce; S.P. Husband; W.R. Furnass; J.B. Boxall. Multivariate Data Mining for Estimating the Rate of Discoloration Material Accumulation in Drinking Water Systems. Procedia Engineering 2014, 89, 173 -180.

AMA Style

S.R. Mounce, S.P. Husband, W.R. Furnass, J.B. Boxall. Multivariate Data Mining for Estimating the Rate of Discoloration Material Accumulation in Drinking Water Systems. Procedia Engineering. 2014; 89 ():173-180.

Chicago/Turabian Style

S.R. Mounce; S.P. Husband; W.R. Furnass; J.B. Boxall. 2014. "Multivariate Data Mining for Estimating the Rate of Discoloration Material Accumulation in Drinking Water Systems." Procedia Engineering 89, no. : 173-180.

Journal article
Published: 23 April 2014 in Procedia Engineering
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Variations in continuously monitored on-line water quality data were investigated to establish whether they could be linked to coliform detections at regulatory monitoring points. We focussed on chlorine residual, turbidity and flow rate at water treatment works (WTW)-A. Archived on-line monitoring data from WTW-A were analysed using cross-correlation and self-organising maps in MATLABĀ® to identify trends in the data running up to coliform detections. The results show that these tools could be developed to help manage WTWs to reduce the number of bacteriological failures. A fingerprint of WTW conditions relating to coliform failures was identified for this case study.

ACS Style

K. Ellis; Stephen Mounce; B. Ryan; M.R. Templeton; Catherine Biggs. Use of On-line Water Quality Monitoring Data to Predict Bacteriological Failures. Procedia Engineering 2014, 70, 612 -621.

AMA Style

K. Ellis, Stephen Mounce, B. Ryan, M.R. Templeton, Catherine Biggs. Use of On-line Water Quality Monitoring Data to Predict Bacteriological Failures. Procedia Engineering. 2014; 70 ():612-621.

Chicago/Turabian Style

K. Ellis; Stephen Mounce; B. Ryan; M.R. Templeton; Catherine Biggs. 2014. "Use of On-line Water Quality Monitoring Data to Predict Bacteriological Failures." Procedia Engineering 70, no. : 612-621.

Journal article
Published: 02 April 2014 in Drinking Water Engineering and Science
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Operational benefits and efficiencies generated using prevalent water industry methods and techniques are becoming more difficult to achieve; as demonstrated by English and Welsh water companies' static position with regards the economic level of leakage. Water companies are often unaware of network incidents such as burst pipes or low pressure events until they are reported by customers; and therefore use reactive strategies to manage the effects of these events. It is apparent that new approaches need to be identified and applied to promote proactive network management if potential operational productivity and standards of service improvements are to be realised. This paper describes how measured flow and pressure data from instrumentation deployed in a UK water distribution network was automatically gathered, checked, analysed and presented using recently developed techniques to generate apposite information about network performance. The work demonstrated that these technologies can provide early warning, and hence additional time to that previously available, thereby creating opportunity to proactively manage a network; for example to minimise the negative impact on standards of customer service caused by unplanned events such as burst pipes. Each method, applied individually, demonstrated improvement on current industry processes. Combined application resulted in further improvements; including quicker and more localised burst main location. Future possibilities are explored, from which a vision of seamless integration between such technologies emerges to enable proactive management of distribution network events.

ACS Style

John Machell; S. R. Mounce; B. Farley; J. B. Boxall. Online data processing for proactive UK water distribution network operation. Drinking Water Engineering and Science 2014, 7, 23 -33.

AMA Style

John Machell, S. R. Mounce, B. Farley, J. B. Boxall. Online data processing for proactive UK water distribution network operation. Drinking Water Engineering and Science. 2014; 7 (1):23-33.

Chicago/Turabian Style

John Machell; S. R. Mounce; B. Farley; J. B. Boxall. 2014. "Online data processing for proactive UK water distribution network operation." Drinking Water Engineering and Science 7, no. 1: 23-33.

Journal article
Published: 21 January 2014 in Water Science and Technology
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Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems.

ACS Style

S. R. Mounce; Will Shepherd; G. Sailor; James Shucksmith; A. J. Saul. Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data. Water Science and Technology 2014, 69, 1326 -1333.

AMA Style

S. R. Mounce, Will Shepherd, G. Sailor, James Shucksmith, A. J. Saul. Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data. Water Science and Technology. 2014; 69 (6):1326-1333.

Chicago/Turabian Style

S. R. Mounce; Will Shepherd; G. Sailor; James Shucksmith; A. J. Saul. 2014. "Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data." Water Science and Technology 69, no. 6: 1326-1333.

Journal article
Published: 01 January 2014 in Procedia Engineering
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Cohesive discolouration material accumulates on the walls of distribution mains and is eroded by increases in boundary shear stress. Understanding how these accumulation and erosion processes vary with material shear strength is important for estimating discolouration risk. To study these relationships, material that had accumulated in two similar pipe systems over three months was eroded by increasing the shear stress in equal increments. By processing the turbidity responses, accumulation and erosion rates were found to be invariant with shear strength at all but the weakest strengths, for which more accumulation was detected

ACS Style

Will Furnass; I. Douterelo; R. Collins; Stephen Mounce; Joby Boxall. Controlled, Realistic-scale, Experimental Study of How the Quantity and Erodibility of Discolouration Material Varies with Shear Strength. Procedia Engineering 2014, 89, 135 -142.

AMA Style

Will Furnass, I. Douterelo, R. Collins, Stephen Mounce, Joby Boxall. Controlled, Realistic-scale, Experimental Study of How the Quantity and Erodibility of Discolouration Material Varies with Shear Strength. Procedia Engineering. 2014; 89 ():135-142.

Chicago/Turabian Style

Will Furnass; I. Douterelo; R. Collins; Stephen Mounce; Joby Boxall. 2014. "Controlled, Realistic-scale, Experimental Study of How the Quantity and Erodibility of Discolouration Material Varies with Shear Strength." Procedia Engineering 89, no. : 135-142.

Journal article
Published: 01 November 2013 in Journal of Water Resources Planning and Management
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Reducing water loss through bursts is a major challenge throughout the developed and developing world. Currently burst lifetimes are often long because awareness and location of them is time- and labor-intensive. Advances that can reduce these periods will lead to improved leakage performance, customer service, and reduce resource wastage. In water-distribution systems the sensitivity of a pressure instrument to change, including burst events, is greatly influenced by its own location and that of the event within the network. A method is described here that utilizes hydraulic-model simulations to determine the sensitivity of potential pressure-instrument locations by sequentially applying leaks to all potential burst locations. The simulation results are used to populate a Jacobian matrix, quantifying the different sensitivities. This matrix may then be searched to identify different instrument locations to achieve required goals: maximising overall sensitivity to all potential events or selective sensitivity to events in different network areas. It is shown here that by searching this matrix to optimize such selective sensitivity, while minimising instrument numbers, it is possible to provide useful burst-localization information. Results are presented from field studies that demonstrate the practical application of the method, showing that current standard network models can provide sufficiently accurate quantification of differential sensitivities and that, once combined with event-detection techniques for data analysis, events can effectively be localized using a small number of instruments.

ACS Style

B. Farley; S. R. Mounce; Joby Boxall. Development and Field Validation of a Burst Localization Methodology. Journal of Water Resources Planning and Management 2013, 139, 604 -613.

AMA Style

B. Farley, S. R. Mounce, Joby Boxall. Development and Field Validation of a Burst Localization Methodology. Journal of Water Resources Planning and Management. 2013; 139 (6):604-613.

Chicago/Turabian Style

B. Farley; S. R. Mounce; Joby Boxall. 2013. "Development and Field Validation of a Burst Localization Methodology." Journal of Water Resources Planning and Management 139, no. 6: 604-613.