| publications-4011 |
article |
2014 |
Afshar, Abbas and Najafi, Ehsan and Najafi, Ehsan |
Consequence management of chemical intrusion in water distribution networks under inexact scenarios |
Journal of Hydroinformatics |
10.2166/hydro.2013.125 |
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The US Environmental Protection Agency (EPA)’s Response Protocol Toolbox provides a list of recommendations on actions that may be taken to minimize the potential threats to public health following a contamination threat. This protocol comprises three steps: (1) detection of contaminant presence, (2) source identification and (3) consequence management. This paper intends to explore consequence management under source uncertainty, applying Minimize Maximum Regret (MMR) and Minimize Total Regret (MTR) approaches. An ant colony optimization algorithm is coupled with the EPANET network solver for structuring the MMR and MTR models to present a robust method for consequence management by selecting the best combination of hydrants and valves for isolation and contamination flushing out of the system. The proposed models are applied to network number 3 of EPANET to present its effectiveness and capabilities in developing effective consequence management strategies. |
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| publications-4012 |
article |
2006 |
Preis, Ami and Ostfeld, Avi |
Contamination Source Identification in Water Systems: A Hybrid Model Trees–Linear Programming Scheme |
Journal of Water Resources Planning and Management |
10.1061/(asce)0733-9496(2006)132:4(263) |
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This paper presents a new approach for contamination source identification in water distribution systems through a coupled model trees-linear programming algorithm. Model trees are an extension of regression trees regression trees: tree-based models used to solve prediction problems in which the response variable is a numerical value in the sense that they associate leaves with multivariate linear models. The model trees replace EPANET through learning i.e., training and cross validation after which a linear programming formulation uses the model trees linear rule classification structure to solve the inverse problem of contamination source identification. The use of model trees represents forward modeling i.e., from root to leaves. The implementation of linear programming on the linear tree structure allows backward inverse modeling i.e., from leaves to root where the contamination injections characteristics are the problem unknowns. The proposed methodology provides an estimation of the time, location, and concentration of the contamination injection sources. The model is demonstrated using two example applications. |
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| publications-4013 |
article |
2010 |
Neupauer, R. M. and Neupauer, Roseanna M. and Records, M. K. and Records, Michael K. and Ashwood, Wesley H. and Ashwood, Wesley H. |
Backward Probabilistic Modeling to Identify Contaminant Sources in Water Distribution Systems |
Journal of Water Resources Planning and Management |
10.1061/(asce)wr.1943-5452.0000057 |
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If a chemical or biological agent is released into a water distribution system, sensors that are installed in the pipe network may detect the contamination as it travels through the system. To minimize the adverse impact of the contaminant release, the source must be characterized to determine the extent of the contamination and to remediate the contaminated area. We present a backward modeling approach that uses the data collected by the sensors to obtain probability density functions that describe the random time in the past that the observed contamination was at a particular upgradient position. These probability density functions can be used to identify the source node and release time. The approach is developed for steady flow conditions with known system demands and for a single, instantaneous source of contamination. Using a hypothetical water distribution system and release scenario, we demonstrate that the backward model is an efficient and effective approach for identifying the source node and t... |
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| publications-4014 |
article |
2013 |
Costa, Diogo and Costa, D. M. and Melo, L. F. and Melo, LuΓÂs F. and Martins, F.G. and Martins, Fernando G. |
Localization of Contamination Sources in Drinking Water Distribution Systems: A Method Based on Successive Positive Readings of Sensors |
Water Resources Management |
10.1007/s11269-013-0431-z |
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This paper addresses the problem of the localization of contamination sources after deliberate contaminations in drinking water distribution systems (DWDS). The proposed methodology is based on the information given by successive positive readings of sensors. Thus, it is possible to estimate the localization of the contamination sources based on only the first sensor that detected a contamination, and then update the results when more information is available. From the tests performed on a real drinking water distribution system, it was possible to observe that as new sensors detect changes in contaminant concentration, other possible contaminations may be detected and the location of contamination sources may be more restricted. The results achieved for the two set of sensors considered in the study contained the correct locations and the instants of contaminations previously simulated. Two case studies were also analysed to study the effect of the occurrence of false positives. It was concluded that it is not always possible to verify the occurrence of those anomalies and when it is verified, it is not possible to distinguish between a false positive and a false negative. The occurrence of false positives did not affect also the results related with the real detections. |
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| publications-4015 |
article |
2013 |
Rasekh, Amin and Brumbelow, Kelly |
Probabilistic Analysis and Optimization to Characterize Critical Water Distribution System Contamination Scenarios |
Journal of Water Resources Planning and Management |
10.1061/(asce)wr.1943-5452.0000242 |
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AbstractCharacterization of critical water distribution system (WDS) contamination scenariosβ€”defined by a set of attributes, a probability of occurrence, and a specific level of consequencesβ€”is a prerequisite for preparation of reliable and cost-effective mitigation, preparedness, and emergency response plans. This study develops Monte Carlo and risk-based optimization schemes to evaluate contamination risk of WDSs for generation of this important class of scenarios, which are representative of the most vulnerable aspects of the system. Defining attributes of contamination scenarios are identified as contaminant type and amount, contamination location, start time, duration, and time of year scenario occurs. Well-documented waterborne outbreaks reported in developed nations are analyzed to empirically estimate statistical characteristics of defining attributes in accidental events. Monte Carlo simulation is conducted to determine the probability distribution of public-health consequences, aggregate conditi... |
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| publications-4016 |
article |
2016 |
Seth, Arpan and Klise, Katherine A. and Siirola, John Daniel and Haxton, Terranna and Laird, Carl D. |
Testing Contamination Source Identification Methods for Water Distribution Networks |
Journal of Water Resources Planning and Management |
10.1061/(asce)wr.1943-5452.0000619 |
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AbstractIn the event of contamination in a water distribution network (WDN), source identification (SI) methods that analyze sensor data can be used to identify the source location(s). Knowledge of the source location and characteristics are important to inform contamination control and cleanup operations. Various SI strategies that have been developed by researchers differ in their underlying assumptions and solution techniques. The following manuscript presents a systematic procedure for testing and evaluating SI methods. The performance of these SI methods is affected by various factors including the size of WDN model, measurement error, modeling error, time and number of contaminant injections, and time and number of measurements. This paper includes test cases that vary these factors and evaluates three SI methods on the basis of accuracy and specificity. The tests are used to review and compare these different SI methods, highlighting their strengths in handling various identification scenarios. The... |
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| publications-4017 |
article |
2016 |
Curnin, Steven and Curnin, Steven and HeumΓΌller, Erich and HeumΓΌller, Erich |
Evaluating emergency management capability of a water utility: A pilot study using exercise metrics |
Utilities Policy |
10.1016/j.jup.2016.01.003 |
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| publications-4018 |
article |
2016 |
Ding, Ning and Ding, Ning and Erfani, Rasool and Erfani, Rasool and Mokhtar, Hamid and Mokhtar, Hamid and Erfani, Tohid and Erfani, Tohid |
Agent Based Modelling for Water Resource Allocation in the Transboundary Nile River |
Water |
10.3390/w8040139 |
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Water resource allocation is the process of assessing and determining a mechanism on how water should be distributed among different regions, sectors and users. Over the recent decades, the optimal solution for water resource allocation has been explored both in centralised and decentralised mechanisms. Conventional approaches are under central planner suggesting a solution which maximises total welfare to the users. Moving towards the decentralised modelling, the techniques consider individuals as if they act selfishly in their own favour. While central planner provides an efficient solution, it may not be acceptable for some selfish agents. The contrary is true as well in decentralised solution, where the solution lacks efficiency leading to an inefficient usage of provided resources. This paper develops a parallel evolutionary search algorithm to introduce a mechanism in re-distributing the central planner revenue value among the competing agents based on their contribution to the central solution. The result maintains the efficiency and is used as an incentive for calculating a fair revenue for each agent. The framework is demonstrated and discussed to allocate water resources along the Nile river basin, where there exist eleven competing users represented as agents in various sectors with upstream-downstream relationships and different water demands and availability. |
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| publications-4019 |
article |
2017 |
Bashi-Azghadi, Seyyed Nasser and Bashi-Azghadi, Seyyed Nasser and Afshar, M. H. and Afshar, Mohammad Hadi and Afshar, Abbas and Afshar, Abbas |
Multi-objective optimization response modeling to contaminated water distribution networks: Pressure driven versus demand driven analysis |
Ksce Journal of Civil Engineering |
10.1007/s12205-017-0447-7 |
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Implementation of management strategies following contamination detection in water distribution networks may extensively change operational mode of nominated valves and hydrants. The commonly used demand driven network solvers may fail to realistically represent system’s performances of new topology due to possible pressure-deficient condition. Realizing their drawbacks, this paper integrates a Pressure Driven Network Solver (PDNS) with multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) in a simulation-optimization scheme. It is illustrated that the two commonly used objective functions, namely minimization of consumed contamination mass and number of polluted nodes, may be in conflict when an operational strategy is implemented. A trade-off is developed to help decision-maker compromise between restraining spatial spread of contaminant and its risk to public health. Decision variables in this optimization model are valve closure and hydrant opening. Each trial solution developed by the NSGA-II addresses a new system topology by changing operational modes of the nominated valves and hydrants. The PDNS determines the nodal pressures and refines the nodal withdraw for trial solution. To illustrate the performance of the proposed methodology, Net3 from EPANET 2 is employed. The results show that the pressure-driven analysis is more realistic and appropriate in comparison with demand-driven analysis in operational conditions. |
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| publications-4020 |
article |
2017 |
Mukherjee, Rabindra Nath and Mukherjee, Rajib and Diwekar, Urmila M. and Diwekar, Urmila M. and Vaseashta, Ashok and Vaseashta, Ashok |
Optimal sensor placement with mitigation strategy for water network systems under uncertainty |
Computers & Chemical Engineering |
10.1016/j.compchemeng.2017.03.014 |
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