Journal of Water Resources Planning and Management

Contamination warning systems (CWSs) are a promising approach for the mitigation of contamination risks in drinking water distribution systems. A critical aspect of the design of a CWS is the strategic placement of online sensors that rapidly detect contaminants. This paper reviews the array of optimization-based sensor placement strategies that have been recently proposed. These strategies are critiqued and several key issues are identified that need to be addressed in future work.

This paper deals with methods aimed at the effective and efficient detection of accidental and/or intentional contaminant intrusion(s) in water distribution systems. The objective of this paper is to present a methodology entitled sensors local optimal transformation system (SLOTS) to address both single-objective and multiobjective sensor layout problems. SLOTS is tested on two benchmark water distribution networks used for the Battle of the Water Sensors Networks challenge (BWSN), held as part of the Water Distribution Systems Analysis Symposium, in Cincinnati in 2006. The objectives considered are detection likelihood and the expected population affected prior to detection. The results obtained demonstrate that SLOTS sensor placements are often near optimal. For both single-objective and multiobjective cases, SLOTS is shown to be capable of identifying placements which are consistently better performing than one of the best BWSN methodologies, the greedy algorithm.

A method for optimizing sensor locations to effectively and efficiently detect contamination in a water distribution network is presented here. The problem is formulated and solved as a twin-objective optimization problem with the objectives being the minimization of the number of sensors and minimization of the risk of contamination. Unlike past approaches, the risk of contamination is explicitly evaluated as the product of the likelihood that a set of sensors fails to detect contaminant intrusion and the consequence of that failure (expressed as volume of polluted water consumed prior to detection). A novel importance-based sampling method is developed and used to effectively determine the relative importance of contamination events, thus reducing the overall computation time. The above problem is solved by using the nondominated sorting genetic algorithm II. The methodology is tested on a case study involving the water distribution system of Almelo (The Netherlands) and the potential intrusion of E. coli bacteria. The results obtained show that the algorithm is capable of efficiently solving the above problem. The estimated Pareto front suggests that a reasonable level of contaminant protection can be achieved using a small number of strategically located sensors.

The design of sensor networks for monitoring contaminants in water distribution systems is currently an active area of research. Much of the effort has been directed at the contamination detection problem and the expression of public health protection objectives. Monitoring networks once they are in place, however, are likely to be used to gather monitoring data for source inversion as well. Thus, the design of these networks with the unique objectives associated with source inversion problems in mind is a necessity. Source inversion problems in water distribution systems are inherently underdetermined and exhibit solution nonuniqueness; and moreover, the structure of the errors associated with a solution are a function of monitoring observations. Optimal inverse experiment design is investigated as an approach for improving solution quality. The approach involves the selection of monitoring locations that are best suited to the generation of a well-conditioned source identification inverse problem. The monitoring design problem is formulated as a nonlinear combinatorial optimization problem and solved using a genetic algorithm. The monitoring designs generated exhibit an optimal substructure that may be exploited to develop more efficient methods of solution. An analysis is conducted to evaluate the source inversion performance of an optimized monitoring network relative to networks designed using different methods. The results of the analysis demonstrate that when the source identification problem is underdetermined, the number of monitoring sensors installed in the network is more important than the method used to locate them.

This paper addresses the issue of contaminated water evacuation as part of the response process in cases of intentional or accidental contamination of drinking water distribution systems (DWDSs). Following a previous study which addressed contaminant isolation, an original flushing procedure (FP) is introduced. Through a heuristic set of rules, the FP defines unidirectional flushing (UDF) strategies in a safe, structured, and efficient manner. In addition to the rather theoretical considerations related to hydraulics modeling, the FP also takes into account the operational issues of UDF implementation. Compared to other similar studies, the latter aspect establishes the main contribution of this paper. The FP has been applied to two real-world networks and application of the heuristic rules is demonstrated with the aid of a detailed example. General results suggest possible improvements to existing network configurations and to the design of projected networks to better support contamination response planning.

The implicit goal of water network design is to provide acceptable service during normal and peak demands and to limit damages to property and people during fires. Despite this, fire damages are seldom explicitly included in network design. This paper presents a single-objective particle swarm optimization program that incorporates a new measure of expected conditional fire damages to size local water distribution mains for fire flow protection in residential service areas. The optimization approach generates trade-off information to help utilities determine the cost effectiveness of adding new pipe capacity to reduce the risk of fire damages in water networks. The optimization program was applied to an 8-pipe network and a 34-pipe network to generate trade-off curves for pipe cost and fire damages. A sensitivity analysis indicated that the level of uncertainty in fire flow had a little impact on pipe sizing and cost in the eight-pipe network. Optimization results in the 34-pipe network supported the industry practice of using a minimum 150-mm distribution main sizing to provide fire flow protection.

This paper presents the energy audit of a water network, which is obtained from the energy equation in integral form, and its time integration extended over a given period (day, month, or year). The analysis allows accounting for all the energy in the system, showing that the energy balance is maintained. This balance can be used to obtain performance indicators to assess the system from the energetic point of view. From these indicators, it is possible to identify the improvement actions that will make the system more efficient. This energy audit requires a previous water balance and the mathematical model of the network, both of which are necessary to know the energy flows through the system’s boundaries.

Systematic transient analysis in water supply systems is intended to determine the reliability of water supply and the integrity of hydraulic devices when hydraulic transients (i.e., water hammers) occur. In the analysis, parameters such as wavespeed, pipe diameter, friction, and others, usually considered as known inputs in traditional water hammer models, are actually subject to uncertainties due to natural or human behaviors. The paper considers these uncertainties and investigates their effects on the reliability of water transportation systems and water supply networks under transient conditions with or without protection devices. In addition, the optimization of transient protection devices is also evaluated based on the probabilistic analysis. The results show that the probabilistic analysis method could inspect the influence of the different input uncertainties on the system responses under transient conditions and provide detailed information on the design for water supply systems and protection devices. A one-dimensional water hammer model of pipe fluid systems and Monte Carlo simulation method are used in this investigation.

In 2002, 2005, and 2006, the U.S. Environmental Protection Agency (EPA) and the Department of the Army entered into memoranda of understanding to implement the Urban Rivers Restoration Initiative (URRI). The URRI is a collaborative process where numerous agencies and stakeholders having different values, interests, and responsibilities come together to agree on actions to restore urban rivers. Much of the literature on collaborative planning addresses topics such as appropriate stakeholder representation, and securing and facilitating participation in planning over time. Less attention is paid to another key to successful collaborative planning processes: utilization of a common planning framework. A common planning framework will help collaborators to develop a shared understanding of the factual basis needed to define the nature and scope of problems and the merits of possible solutions. This paper identifies how the analytical frameworks of the principal URRI participating agencies (U.S. Army Corps of Engineers and EPA) can be integrated to serve the goals of the URRI in ways that highlight incremental effects on costs and benefits from choosing among alternative plans and presented in a manner enabling collaborators to compare the benefits from different combinations and permutations of all the potential measures for given budgets. The conclusion discusses the contribution of the integrated planning framework to building consensus among federal agencies, nonfederal agencies, and other stakeholders on a URRI implementation plan.