| publications-4271 |
article |
2007 |
McKenna, Sean Andrew and Hart, David and Hart, Darren M. and Klise, Katherine A. and Klise, Katherine A. and Cruz, Victoria and Cruz, Victoria and McKenna, Sean A. and Wilson, M. P. and Wilson, Mark and Wilson, M. and Wilson, M. |
Event Detection from Water Quality Time Series |
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10.1061/40927(243)518 |
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Detection of anomalous events in water distribution systems is of interest for both daily operations focused on delivery of high quality water as well as for identification of accidental or intentional contamination events. In lieu of network-wide deployment of in-situ contaminant-specific sensors, data streams resulting from in-situ monitoring of ambient water quality are employed as input to event detection algorithms to identify periods of anomalous water quality. The basis of these approaches is prediction of the future water quality values (state estimation) and then comparison of the prediction errors, the differences between predicted and measured water quality signals, to identify outliers in an on-line framework. These algorithms generally rely on a stationary time series and large, sudden changes within the time series make outlier detection difficult. Here we propose an approach to improving the identification of events, defined as a cluster of outliers, that will also identify changes in the baseline water quality. This approach is called the binomial event discriminator (BED) and it uses a failure model based on the binomial distribution to determine the probability of an event existing based on r outliers occurring within n time steps. If the consecutive number of outliers exceeds an upper limit, a change in the baseline water quality is declared. The BED is applied to observed water quality collected at a location within a utility distribution system. The BED is able to reduce the number of false positive event identifications by several orders of magnitude compared to not using the BED. The BED is also identifies two locations as baseline water quality changes. |
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| publications-4272 |
article |
2008 |
Worthington, Andrew C. and Worthington, Andrew C. and Hoffman, Mark and Hoffman, Mark |
AN EMPIRICAL SURVEY OF RESIDENTIAL WATER DEMAND MODELLING |
Journal of Economic Surveys |
10.1111/j.1467-6419.2008.00551.x |
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The increased reliance on demand-side management policies as an urban water consumption management tool has stimulated considerable debate among economists, water utility managers, regulators, consumer interest groups and policymakers. In turn, this has fostered an increasing volume of literature aimed at providing best-practice estimates of price and income elasticities, quantifying the impact of non-price water restrictions and gauging the impact of non-discretionary environmental factors affecting residential water demand. This paper provides a synoptic survey of empirical residential water demand analyses conducted in the last 25 years. Both model specification and estimation and the outcomes of the analyses are discussed. |
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| publications-4273 |
article |
2008 |
Liu, Li and Liu, Li and Zechman, Emily M. and Zechman, Emily M. and Brill, E. Downey and Brill, E. Downey and Mahinthakumar, G. and Mahinthakumar, Gnanamanikam and Ranjithan, S. Ranji and Ranjithan, S. and Uber, James G. and Uber, James G. |
Adaptive Contamination Source Identification in Water Distribution Systems Using an Evolutionary Algorithm-based Dynamic Optimization Procedure |
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10.1061/40941(247)123 |
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Accidental drinking water contamination has long been and remains a major threat to water security throughout the world. Consequently, contamination source identification is an important and difficult problem in the managing safety in water distribution systems. This problem involves the characterization of the contaminant source based on observations that are streaming from a set of sensors in the distribution network. Since contamination spread in a water distribution network is relatively quick and unpredictable, rapid identification of the source location and related characteristics is important to take contaminant control and containment actions. As the contaminant event unfolds, the streaming data could be processed over time to adaptively estimate the source characteristics. This provides an estimate of the source characteristics at any time after a contamination event is detected, and this estimate is continually updated as new observations become available. We pose and solve this problem using a dynamic optimization procedure that could potentially provide a real-time response. As time progresses, additional data is observed at a set of sensors, changing the vector of observations that should be predicted. Thus, the prediction error function is updated dynamically, changing the objective function in the optimization model. We investigate a new multi population-based search using an evolutionary algorithm (EA) that at any time represents the solution state that best matches the available observations. The set of populations migrates to represent updated solution states as new observations are added over time. At the initial detection period, non-uniqueness is inherent in the source-identification due to inadequate information, and, consequently, several solutions may predict similarly well. To address nonuniqueness at the initial stages of the search and prevent premature convergence of the EA to an incorrect solution, the multiple populations in the proposed methodology are designed to maintain a set of alternative solutions representing different non-unique solutions. As more observations are added, the EA solutions not only migrate to better solution states, but also reduce the number of solutions as the degree of non-uniqueness diminishes. This new dynamic optimization algorithm adaptively converges to the best solution(s) to match the observations available at any time. The new method will be demonstrated for a contamination source identification problem in an illustrative water distribution network. |
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| publications-4274 |
article |
2008 |
Kenney, Douglas S. and Kenney, Douglas S. and Goemans, Christopher and Goemans, Christopher and Klein, Roberta and Klein, Roberta and Lowrey, Jessica L. and Lowrey, Jessica L. and Reidy, Kevin and Reidy, Kevin |
Residential Water Demand Management: Lessons from Aurora, Colorado1 |
Journal of The American Water Resources Association |
10.1111/j.1752-1688.2007.00147.x |
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Abstract: Residential water demand is a function of several factors, some of which are within the control of water utilities (e.g., price, water restrictions, rebate programs) and some of which are not (e.g., climate and weather, demographic characteristics). In this study of Aurora, Colorado, factors influencing residential water demand are reviewed during a turbulent drought period (2000-2005). Findings expand the understanding of residential demand in at least three salient ways: first, by documenting that pricing and outdoor water restriction policies interact with each other ensuring that total water savings are not additive of each program operating independently; second, by showing that the effectiveness of pricing and restrictions policies varies among different classes of customers (i.e., low, middle, and high volume water users) and between predrought and drought periods; and third, in demonstrating that real-time information about consumptive use (via the Water Smart Reader) helps customers reach water-use targets. |
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| publications-4275 |
article |
2011 |
Aksela, Kia and Aksela, Kia and Aksela, Matti and Aksela, Matti |
Demand Estimation with Automated Meter Reading in a Distribution Network |
Journal of Water Resources Planning and Management |
10.1061/(asce)wr.1943-5452.0000131 |
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Accurate estimation and prediction of the demand patterns of the customers of a water works would enable more accurate network hydraulic management. In this study, a probabilistic model to generate residential demand patterns for single-family and semidetached houses is formed. To form these pattern models, an automated meter reading technique has been utilized to gather the necessary information from a sample of residences, which are used to model the demand behavior in a way that is applicable to a much wider range of residences. A linear regression model was constructed to predict the measured average weekly consumption from the calculated average weekly consumption. The residences were clustered by their weekly water demand into four distinct classes using the k-means algorithm. For the final result, probability models developed on the basis of mixtures of Gaussians for each class, in conjunction with the prediction model of the weekly water consumption, were utilized so that estimates for the demand ... |
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| publications-4276 |
article |
2012 |
An, Li and An, Li |
Modeling human decisions in coupled human and natural systems: Review of agent-based models |
Ecological Modelling |
10.1016/j.ecolmodel.2011.07.010 |
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| publications-4277 |
article |
2020 |
Jones, David and Jones, David Edward and Jones, David and Snider, Chris and Snider, Chris and Nassehi, Aydin and Nassehi, Aydin and Yon, Jason and Yon, Jason and Hicks, Ben and Hicks, Ben J |
Characterising the Digital Twin: A systematic literature review |
Cirp Journal of Manufacturing Science and Technology |
10.1016/j.cirpj.2020.02.002 |
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Abstract While there has been a recent growth of interest in the Digital Twin, a variety of definitions employed across industry and academia remain. There is a need to consolidate research such to maintain a common understanding of the topic and ensure future research efforts are to be based on solid foundations. Through a systematic literature review and a thematic analysis of 92 Digital Twin publications from the last ten years, this paper provides a characterisation of the Digital Twin, identification of gaps in knowledge, and required areas of future research. In characterising the Digital Twin, the state of the concept, key terminology, and associated processes are identified, discussed, and consolidated to produce 13 characteristics (Physical Entity/Twin; Virtual Entity/Twin; Physical Environment; Virtual Environment; State; Realisation; Metrology; Twinning; Twinning Rate; Physical-to-Virtual Connection/Twinning; Virtual-to-Physical Connection/Twinning; Physical Processes; and Virtual Processes) and a complete framework of the Digital Twin and its process of operation. Following this characterisation, seven knowledge gaps and topics for future research focus are identified: Perceived Benefits; Digital Twin across the Product Life-Cycle; Use-Cases; Technical Implementations; Levels of Fidelity; Data Ownership; and Integration between Virtual Entities; each of which are required to realise the Digital Twin. |
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| publications-4278 |
article |
1985 |
Males, Richard M. and Clark, Robert M. and Wehrman, Paul J. and Gates, William E. |
Algorithm for Mixing Problems in Water Systems |
Journal of Hydraulic Engineering |
10.1061/(asce)0733-9429(1985)111:2(206) |
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The β€_x009c_Solverβ€_x009d_ algorithm, developed as an outgrowth of work on cost allocation in water distribution systems, is a simple technique that solves a number of interesting problems in water distribution system analysis. Problems related to mixing water from different sources within the distribution network, travel time from any source to any node of the network, and development of the cost of service to any node in the network can be solved assuming steadyβ€state conditions, given a prior solution of the hydraulics (flow in each link) of the network. All these problems are formulated as the solution of simultaneous linear equations. The Solver algorithm has been coded in Fortran and incorporated within the structure of a multiβ€purpose system of computer programs that allows for the storage, display and manipulation of data associated with nodeβ€link networks describing water distribution systems. These programs are identified by the name Water Supply Simulation Model. The formulation of the Solver algorithm is de... |
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| publications-4279 |
article |
1994 |
Boulos, Paul F. and Boulos, Paul F. and Altman, Tom and Altman, Tom and Jarrige, Pierre-Antoine and Jarrige, Pierre-Antoine and Collevati, Francois and Collevati, Francois |
An event-driven method for modelling contaminant propagation in water networks |
Applied Mathematical Modelling |
10.1016/0307-904x(94)90163-5 |
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Abstract An efficient computer-oriented methodology is presented for use in analyzing water quality variations in drinking-water distribution systems. The proposed method can be effectively used for modelling chemical, biological, and hydraulic changes that result from distribution system activities and to predict the transient distribution of contaminants throughout the pipe system. It is predicated on the material mass balance accounting for transport and kinetic reaction processes. Perfect advective one-dimensional displacement with complete mixing of material at the network nodes is assumed. The method is event-driven and determines the optimal pipe segmentation scheme with the smallest number of segments necessary to carry out the simulation process. The resulting approach allows for dynamic water quality modelling that is less sensitive to the structure of the network and to the length of the simulation than previously proposed methods. In addition, numerical dispersion of concentration profile resolution is eliminated. The applicability of the method is illustrated using an example water distribution network. Enhancement of distribution system water quality management is a principal benefit of the methodology. |
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| publications-4280 |
article |
1994 |
Liggett, James A. and Liggett, James A. and Chen, Liβ€Chung and Chen, Li-Chung |
Inverse Transient Analysis in Pipe Networks |
Journal of Hydraulic Engineering |
10.1061/(asce)0733-9429(1994)120:8(934) |
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Modern monitoring devices can inexpensively extract a huge amount of data from water-distribution systems through measurements of pressure (and sometimes flows). These data can be used in algorithms for transient analysis, time-lagged calculations, inverse calculations, and event detection to continuously determine the calibration and the general state of health of the distribution system. The last three calculations depend on the first. The most useful of those three is the inverse calculation, which can calibrate while determining leaks or unauthorized use. A key to efficient calculation is the adjoint solution of the system (generally easier than the transient analysis) to find gradient data and a Jacobian matrix. These are used to find a Hessian matrix, which is used in the Levenberg-Marquardt method to adjust parameters so as to minimize the difference between calculated and measured heads. The adjoint method is also used to compute sensitivities, which are valuable in judging the quality of the solution. |
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