| publications-4291 |
article |
2008 |
Sanctis, Annamaria E. De and Sanctis, Annamaria E. De and Shao, Feng and Shang, Feng and Uber, James G. and Uber, James G. |
DETERMINING POSSIBLE CONTAMINANT SOURCES THROUGH FLOW PATH ANALYSIS |
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10.1061/40941(247)124 |
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The development of water quality sensors and sensor technologies makes it feasible to establish water quality monitoring networks in drinking water distribution systems. The collected water quality data can be used to determine the location and time of the source contamination in the case of terrorist attack or accidental contamination intrusion. If the contaminant reaction can be modeled reliably within the pipe network and the sensors can measure water quality quantitatively, minimization of the difference between modeled and measured water quality is one approach to solution of the contaminant source determination problem. The underling mathematical problem is, however, inherently ill-posed, due to the shortage of measurements compared to source parameters, and regularization methods are required to force identification of a unique solution. Further, it is usually the case that the contaminant reaction dynamics are unknown, and/or the sensor can only detect the presence or absence of the contaminant and not the quantitative concentration. Even if the source determination problem can be formulated mathematically and optimization algorithms can be applied to solve the problem, the decision variable dimension can be huge since contamination can happen anywhere and anytime in the network. An alternative practical method is developed in this paper to identify all possible locations and times that explain contamination incidents detected by the water quality sensors. It is assumed that contaminant injections occur at network junctions and over discrete time intervals. The method only requires the positive/negative sensor status over time, and knowledge of network hydraulics. A particle backtracking algorithm is used to identify the water flow paths leading to each sensor measurement and the travel time from the junctions along the flow paths to the measurement. Those locations and times that are connected to positive sensor measurements – and are not connected to negative measurements – are the possible sources, assuming no false positive/negative readings and an accurate hydraulic model. This method can also be used as a pruning step for solving the source identification problem using optimization algorithms, as it reduces the number of decision variables by eliminating locations and times that are inconsistent with the sensor responses. The method also forms the basis for incorporating important concerns about hydraulic and sensor uncertainty, which are likely to enlarge the set of possible sources. |
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| publications-4292 |
article |
2013 |
Fielding, Kelly S. and Fielding, Kelly S. and Spinks, Anneliese and Spinks, Anneliese and Russell, Sally and Russell, Sally and McCrea, Rod and McCrea, Rod and Stewart, Rodney Anthony and Stewart, Rodney Anthony and Gardner, John and Gardner, John and Gardner, John |
An experimental test of voluntary strategies to promote urban water demand management |
Journal of Environmental Management |
10.1016/j.jenvman.2012.10.027 |
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| publications-4293 |
article |
2013 |
Nguyen, Khoi and Nguyen, Khoi Anh and Nguyen, Khoi and Nguyen, Khoi and Nguyen, Khoi Anh and Stewart, Rodney Anthony and Stewart, Rodney Anthony and Zhang, Hong and Zhang, Hong |
An intelligent pattern recognition model to automate the categorisation of residential water end-use events |
Environmental Modelling and Software |
10.1016/j.envsoft.2013.05.002 |
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The rapid dissemination of residential water end-use (e.g. shower, clothes washer, etc.) consumption data to the customer via a web-enabled portal interface is becoming feasible through the advent of high resolution smart metering technologies. However, in order to achieve this paradigm shift in residential customer water use feedback, an automated approach for disaggregating complex water flow trace signatures into a registry of end-use event categories needs to be developed. This outcome is achieved by applying a hybrid combination of gradient vector filtering, Hidden Markov Model (HMM) and Dynamic Time Warping Algorithm (DTW) techniques on an existing residential water end-use database of 252 households located in South-east Queensland, Australia having high resolution water meters (0.0139?L/pulse), remote data transfer loggers (5?s logging) and completed household water appliance audits. The approach enables both single independent events (e.g. shower event) and combined events (i.e. several overlapping single events) to be disaggregated from flow data into a comprehensive end-use event registry. Complex blind source separation of concurrently occurring water end use events (e.g. shower and toilet flush occurring in same time period) is the primary focus of this present study. Validation of the developed model is achieved through an examination of 50 independent combined events. |
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| publications-4294 |
article |
2013 |
Lee, Jay and Lee, Jay and Lee, Jay and Lee, Jay and Lapira, Edzel and Lapira, Edzel and Bagheri, Behrad and Bagheri, Behrad and Kao, Hung-An and Kao, Hung-An |
Recent advances and trends in predictive manufacturing systems in big data environment |
Manufacturing letters |
10.1016/j.mfglet.2013.09.005 |
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| publications-4295 |
article |
2001 |
Berger, T. and Berger, Thomas |
Agentβ€based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis |
Agricultural Economics |
10.1111/j.1574-0862.2001.tb00205.x |
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This paper presents a spatial multi-agent programming model, which has been developed for assessing policy options in the diffusion of innovations and resource use changes. Unlike conventional simulation tools used in agricultural economics, the model class described here applies a multi-agent/cellular automata (CA) approach by using heterogeneous farm-household models and capturing their social and spatial interactions explicitly. The individual choice of the farm-household among available production, consumption, investment and marketing alternatives is represented in recursive linear programming models. Adoption constraints are introduced in form of network-threshold values that reflect the cumulative effects of experience and observation of peers' experiences. The model's economic and hydrologic components are tightly connected into a spatial framework. The integration of economic and hydrologic processes facilitates the consideration of feedback effects in the use of water for inigation. The simulation runs of the model are carried out with an empirical data set, which has been derived from various data sources on an agricultural region in Chile. Simulation results show that agent-based spatial modelling constitutes a powerful approach to better understanding processes of innovation and resource use change. Β© 2001 Elsevier Science B.V. All rights reserved. |
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| publications-4296 |
article |
2003 |
Newman, M. E. J. and Newman, Mark |
The Structure and Function of Complex Networks |
Siam Review |
10.1137/s003614450342480 |
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Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks. |
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| publications-4297 |
article |
2006 |
Khanal, Nabin and Khanal, Nabin and Buchberger, Steven G. and Buchberger, Steven G. and McKenna, Sean Andrew and McKenna, Sean A. |
Distribution System Contamination Events: Exposure, Influence, and Sensitivity |
Journal of Water Resources Planning and Management |
10.1061/(asce)0733-9496(2006)132:4(283) |
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This paper presents a two-part investigation of the response of municipal water distribution systems to contamination events. In Part I of the investigation, a contamination event was modeled as a steady 6-h injection of a soluble conservative substance into a single node. The injection was repeated node by node at all 89 nodes in the pipe network. In each case, the fraction of the population at risk of contaminant exposure was estimated at the end of a 72-h simulation period. A dimensionless exposure index (EI) was introduced as a simple global measure of network response, ranging from EI=0 (no consumers are exposed) to EI=100\% (all consumers are at risk of exposure). Simulation results were used to construct a zone of influence (ZOI) map, which categorizes network injection nodes on the basis of their potential to expose downstream consumers. In Part II of the investigation, a generalized sensitivity analysis was performed to determine the sensitivity of network response to four dynamic network variable... |
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| publications-4298 |
article |
2008 |
Eliades, Demetrios G. and Polycarpou, Marios M. |
ITERATIVE DEEPENING OF PARETO SOLUTIONS IN WATER SENSOR NETWORKS |
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10.1061/40941(247)114 |
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This paper investigates the issue of finding the best sensor locations in a drinking water distribution network for detecting harmful substances. The problem is formulated in a multi-objective optimization framework with five performance measures: time of detection, population affected prior to detection, demand of contaminated water, detection likelihood and demand coverage. In practical application, due to the large size of water distribution networks, the space of possible solutions expands dramatically, making it difficult or impossible to determine the optimal solutions. We propose the β€_x009c_Iterative Deepening of Pareto Solutionsβ€_x009d_ search algorithm, for locating β€_x009c_good enoughβ€_x009d_ solutions. The algorithm solves the problem by iteratively choosing the best non-dominant solutions, and expanding them by increasing the depth of the search tree until all the sensors have been used. Simulation experiments were performed on two water distribution networks, following the formulation defined in the β€_x009c_Battle of the Water Sensor Networksβ€_x009d_ design challenge (Ostfeld et al. 2005). Four contamination scenarios are considered and from the sets of possible solutions, the most appropriate designs are proposed. |
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| publications-4299 |
article |
2008 |
Ghiassi, M. and Ghiassi, M. and Zimbra, David and Zimbra, David and Saidane, Hassine and Saidane, H. |
Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model |
Journal of Water Resources Planning and Management |
10.1061/(asce)0733-9496(2008)134:2(138) |
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This paper presents the development of a dynamic artificial neural network model (DAN2) for comprehensive urban water demand forecasting. Accurate short-, medium-, and long-term demand forecasting provides water distribution companies with information for capacity planning, maintenance activities, system improvements, pumping operations optimization, and the development of purchasing strategies. We examine the effects of including weather information in the forecasting models and show that such inclusion can improve accuracy. However, we demonstrate that by using time series water demand data, DAN2 models can provide excellent fit and forecasts without reliance upon the explicit inclusion of weather factors. All models are validated using data from an actual water distribution system. The monthly, weekly, and daily models produce forecasting accuracies above 99\%, and the hourly models above 97\%. The excellent model accuracy demonstrates the effectiveness of DAN2 in forecasting urban water demand across al... |
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| publications-4300 |
article |
2010 |
Perelman, Lina and Ostfeld, Avi |
Extreme Impact Contamination Events Sampling for Water Distribution Systems Security |
Journal of Water Resources Planning and Management |
10.1061/(asce)0733-9496(2010)136:1(80) |
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In recent years, drinking water distribution systems security has become a major concern. To protect public health and minimize the effected community by a contaminant intrusion, water quality needs to be continuously monitored and analyzed. Contamination warning systems are being designed to detect and characterize contaminant intrusions into water distribution systems. Since contamination injections can occur at any node at any time the theoretical number of possible injection events, even for a medium-size network, is huge and grows substantially with system size. As a result of that contamination warning systems are designed based on a subset of contamination events, which is not necessarily the most critical. To cope with this difficulty a method derived from cross entropy, which originates from rare event simulations, is proposed. The suggested algorithm is able to sample efficiently a rare subset (i.e., a subset of events with a small probability to occur, but with an extreme impact) of the entire ... |
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