Journal of Water Resources Planning and Management

Since the 1960s, researchers have continued to develop new methodologies and algorithms in support of the planning, design, and management of water distribution systems. While initial research focused on modeling the hydraulics of such systems, the 1980s gave rise to additional research focus on water quality issues. More recent research has expanded into issues of system reliability and resilience, energy management, and sensor and chlorine booster station placement. In most cases, researchers have relied on either hypothetical water distribution systems or a handful of actual systems for use as benchmark test systems. Some of the more widely used actual systems include the New York tunnel system and the Hanoi water distribution system. This paper describes the development of a database of several water distribution systems synthesized from a statewide database of systems originally developed by the Kentucky Infrastructure Authority. The developed models include both small and medium networks, as well a range of system characteristics (i.e., system configuration—grid, looped, and branched systems—and system components—number of tanks, pump stations, and supply sources). The development of the database is described, as well as how to gain access to the developed models. All models have been developed to support use by water distribution system modeling software.

Flow measurement data at the district meter area (DMA) level has the potential for burst detection in the water distribution systems. This work investigates using a polynomial function fitted to the historic flow measurements based on a weighted least-squares method for automatic burst detection in the U.K. water distribution networks. This approach, when used in conjunction with an expectation-maximization (EM) algorithm, can automatically select useful data from the historic flow measurements, which may contain normal and abnormal operating conditions in the distribution network, e.g., water burst. Thus, the model can estimate the normal water flow (nonburst condition), and hence the burst size on the water distribution system can be calculated from the difference between the measured flow and the estimated flow. The distinguishing feature of this method is that the burst detection is fully unsupervised, and the burst events that have occurred in the historic data do not affect the procedure and bias the burst detection algorithm. Experimental validation of the method has been carried out using a series of flushing events that simulate burst conditions to confirm that the simulated burst sizes are capable of being estimated correctly. This method was also applied to eight DMAs with known real burst events, and the results of burst detections are shown to relate to the water company’s records of pipeline reparation work.

Effective management of aging water distribution infrastructure is essential for preserving the economic vitality of North American municipalities. Historical failures within Scarborough, Ontario, Canada, reveal a seasonal pattern to water main failures, with the majority of failures occurring during the very cold winter months. Extensive installation of cement mortar lining and cathodic protection have extended the life span of aging water mains and reduced escalating failure rates. Artificial neural networks are found to be capable of predicting the time to failure for individual pipes using a range of pipe-specific attributes, including diameter, length, soil type, construction year, and the number of previous failures. The developed models have correlation coefficients ranging from 0.70–0.82 on instances reserved for evaluating predictive performance and have utility on an asset-by-asset basis when planning water main inspection, maintenance, and rehabilitation. Simulated failure scenarios indicate a return to high failure rates if cement mortar lining and cathodic protection are not extended to all candidate pipes in the distribution system.

Commonly, water distribution networks have many treed or branched subgraphs. The equations for these systems are often solved for the steady-state flows and heads with a fast implementation of Newton’s method such as the global gradient algorithm (GGA). Applying the GGA to the whole of a network that has a treed portion means using a nonlinear solver on a problem that has separable linear and nonlinear parts. This is not optimal, and the flows and heads of treed networks can be found more quickly if the flows and heads of the treed portions are first solved explicitly by a linear process and then only the flows and heads of the smaller looped part of the network are found using the nonlinear GGA solver. The main contributions in this paper are the following: (1) development of a forest-core partitioning algorithm (FCPA), which separates the linear treed part of the network (the forest) from the nonlinear looped part (the core) by inspecting the incidence matrix; this allows the linear and nonlinear parts of the problem to be solved separately by linear and nonlinear methods, respectively; (2) explaining the mathematical basis for the adjustment of the network as the forest is identified and relating the mathematics to adjusting the graph of the network; (3) demonstration of flop count savings of between approximately 40 and 70% achievable in the linear phase of the GGA with forest-core partitioning on eight realistic case study water distribution networks ranging in size from 932 to 19,647 pipes; these savings lead, in turn, to savings in total CPU times of between 11 and 31% on the same networks; and (4) removing the need to use special techniques to deal with zero flows in forest pipes that have head loss modeled by the Hazen-Williams formulation. Where zero flows occur in the core, as a result of equal heads at the two ends of a pipe, special techniques will still need to be used.

This paper presents a method for the reduction of network models described by a system of nonlinear algebraic equations. Such models are, for example, present when modeling water networks, electrical networks, and gas networks. The approach calculates a network model equivalent to the original one, but containing fewer components. This procedure has an advantage compared with straightforward linearization because the reduced nonlinear model preserves the nonlinearity of the original model and approximates the original model in a wide range of operating conditions. The method is applicable to hydraulic analysis and has been validated by simplifying many practical water network models for optimization studies.

This paper presents a new methodology for the automated near-real-time detection of pipe bursts and other events that induce similar abnormal pressure/flow variations (e.g., unauthorized consumptions) at the district metered area (DMA) level. The new methodology makes synergistic use of several self-learning artificial intelligence (AI) techniques and statistical data analysis tools, including wavelets for denoising of the recorded pressure/flow signals, artificial neural networks (ANNs) for the short-term forecasting of pressure/flow signal values, statistical process control (SPC) techniques for short- and long-term analysis of the pipe burst/other event-induced pressure/flow variations, and Bayesian inference systems (BISs) for inferring the probability of a pipe burst/other event occurrence and raising corresponding detection alarms. The methodology presented here is tested and verified on a case study involving several DMAs in the United Kingdom (U.K.) with both real-life pipe burst/other events and engineered (i.e., simulated by opening fire hydrants) pipe burst events. The results obtained illustrate that it can successfully identify these events in a fast and reliable manner with a low false alarm rate.

Reliability assessment of water supply systems (WSSs) is an important aspect of WSS planning and operations. Traditionally, WSS reliability involves the comparison of hydraulics (e.g., pressure or available water volume) and water quality (e.g., residual chlorine) parameters with their desired minimum level of service under various emergency loading conditions. To compute hydraulic dependent parameters (e.g., pressures, flow), different algorithms solve continuity and energy equations expressed in terms of certain independent parameters (e.g., roughness parameters and nodal demands) with certainty. Similarly, transport equations expressed in terms of different quality parameters are solved deterministically to compute water quality dependent parameters (e.g., residual chlorine). However, it is extremely challenging, even impossible, to estimate network-independent parameters with certainty. Therefore, estimated dependent parameters based on semiquantitative information bear uncertainty, which leads to a question of how reliable current reliability assessment results are. To address this issue, this paper proposes a new means of reliability assessment in which the reliability of a WSS is expressed in terms of utility and associated beliefs. The methodology developed provides an estimate of the uncertainties inherent in traditional reliability assessment results. It is expected that the proposed methodology will help municipalities to make informed decisions in order to increase the safety and security of public health.

In recent decades, there has been a rapid rate of development of hydropower in China. The unprecedented rate of expansion, development scale, and large numbers of hydropower plants have posed a challenge to the operation of large-scale cascaded hydropower systems (OLCHSs), which has become one of the most important factors in ensuring the security and economic operation of the power grid in China. In this paper, a long-term optimal operation model is developed for the purpose of maximizing the total generated energy of cascaded hydropower plants. To solve the OLCHS problem effectively, an elite-guide particle swarm optimization (EGPSO) algorithm is proposed in this paper. An external archive set, which can preserve elite solutions during the evolution process, is employed to provide flying directions for particles. Since the OLCHS problem is a high-dimensional, nonlinear, multistage, and stringent constraint optimal problem, the proposed algorithm introduces three new innovations: a layer-partition approach is presented to divide the decision vectors into small ones according to the reservoir’s relative position and hydraulic connection. Meanwhile, the initial solutions are generated in the proposed contractively feasible region so that operation results do not rely so much on the initial solution. To deal with the multiconstraint coupling problem, a constraint-corridor method is adapted to handle these complex constraints in the cascaded hydropower system. Finally, this novel strategy is applied successfully to solve the optimal operation of a large-scaled cascaded hydropower system in the upper reaches of the Yangtze River. Compared with the conventional method, the proposed EGPSO has a competitive performance in not only simulation results but also computing time, which offers a new approach to solving high-dimensional and complicated problems of optimizing reservoir dispatching.

The integration of quality and quantity issues in the management of water resources systems is key to meet society’s long-term needs for freshwater while maintaining essential ecological services and economic benefits. Current water management practices are mostly targeted towards quantitative uses, and quality is usually addressed separately as an independent problem. One of the reasons for the lack of integration lies in the inadequacy of optimization techniques nowadays available to cope with the large, distributed, simulation models adopted to characterize the coupled ecological and biochemical processes in water bodies. In this paper we propose a novel approach based on the conjunctive use of a batch-mode Reinforcement Learning algorithm and a one-dimensional (1D) coupled hydrodynamic-ecological model to design the optimal operation of a multipurpose water reservoir accounting for both quantity and quality targets. We consider up to five operating objectives, including both in-reservoir and downstream water quality parameters, and design efficient operating policies conditioned upon not only the current storage but also water characteristics, such as temperature and total suspended solids at different depths. The approach is applied to a real world case study in Japan consisting of a water reservoir, Tono Dam, equipped with a selective withdrawal structure and used for flood protection, irrigation and recreational purposes. Results show that a potential control over in-reservoir and downstream water quality can be gained without impairing the hydraulic capacity of the reservoir by effectively exploiting—through the operating policy—the operational flexibility provided by the selective withdrawal structures.

The sustainable urban water management paradigm is based on the idea that water supply, storm water drainage, and wastewater disposal are interrelated resources that can increase the sustainability at the urban scale. In this context, the construction of reservoirs mainly fed by storm water and operated for drinking supply purposes can be demonstrated to achieve long-term sustainability objectives. However, the operational management of reservoirs located in urban areas faces a number of challenges due to the high hydraulic efficiency of urban catchments, i.e., short time of concentration, increased runoff rates, and losses of infiltration and baseflow. With the purpose of discussing the best alternatives to deal with these extreme hydrological features, this article analyzes the performance of offline and online operation, based on stochastic dynamic programming and deterministic model predictive control. Moreover, a quantitative assessment of the role of the hydro-meteorological information available in real time is provided. The case study considered is Marina Reservoir, a multipurpose reservoir located in the heart of Singapore. It is characterized by a large, highly urbanized catchment that produces significant discharges with a short time of concentration of approximately 1 h. Results show that the online approach, by anticipating the inflow events, outperforms the offline one and provides a better compromise between floods control, pumps usage, and drinking water supply objectives.

In many areas of the country, urban storm water runoff is a growing water quality problem. To improve storm water programs, regulatory officials are now placing greater emphasis on reducing the volume of runoff. Called green infrastructure or low-impact development, storm water control is increasingly focused on reducing the amount of urban impervious land cover and using vegetative conveyance systems and infiltration areas to reduce negative environmental impacts. While many green infrastructure practices reduce capital infrastructure costs, less is known about the opportunity costs and pecuniary benefits to developers from implementation of green infrastructure to reduce storm water effects. This paper presents a hedonic property-value model of residential property sale prices regressed on property and location characteristics, which include differing infrastructure design features that affect storm water runoff (street widths, cul-de-sacs, curb-and-gutters). The data include 1,360 single-family property transactions in Hanover County, Virginia that occurred in 1995 and 1996. Results suggest that prohibiting curb-and-gutters and cul-de-sacs would impose an opportunity cost on developers; buyers are willing to pay 1.3 and 7.8% property-price premiums for homes located on a street with a cul-de-sac and curb-and-gutters, respectively. Constructing narrower streets would provide a pecuniary benefit to developers because people would pay approximately 3.5% more for these properties.

Anticipatory water management uses weather forecasts and water system simulation models to take operational water management actions before an event occurs. In this paper a framework consisting of the different steps and challenges of developing and evaluating an anticipatory water management strategy is discussed. The framework makes use of recent developments in ensemble weather forecasting and modeling of controlled water systems. In particular, flexible water-system control models enable simulation of a wide range of control strategies in multiyear hindcast analyses. With the increasing number of archives for weather forecasts and water-system-state variables, and the reduction in computational time resulting from increasing processor speed and parallel computing, hindcast verification analysis will become the basis for development and optimization of new operational water management strategies. The framework is successfully applied to a flood control case study in the Netherlands. Archived ensemble precipitation forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) were fed into a simulation model of the water system to create water-level hindcasts. Decision rules to take anticipatory flood control actions on the basis of these ensemble forecasts have been developed. The optimized decision rules proved successful in reducing total damage costs resulting from deviations from target water levels for an analysis period of seven years.

Climate projections in many U.S. metropolitan areas show increases in daily temperature, and potential shifts in rainfall patterns toward stronger, more intense storms. At the same time, metropolitan regions continuing to experience population growth face the prospect of areal expansion and higher land-use intensities. The possible coupling of such developmental changes and more frequent intense precipitation events poses a litany of well-known challenges to storm-water management. This paper reports survey research that examines the experiences and expectations of engineers, planners, and other agency staff in storm-water management in the Washington-Baltimore metropolitan region in the context of climate change. We find that staff from jurisdictions with a high degree of development and strong population growth, and those with more work experience, engagement with climate issues in their work, and greater belief that scientists understand climate change, exhibit a higher level of concern with the potential threat of climate change on storm water runoff.

A noncrossover dither creeping mutation-based genetic algorithm (CMBGA) for pipe network optimization has been developed and is analyzed in this paper. This CMBGA differs from the classic genetic algorithm (GA) optimization in that it does not utilize the crossover operator; instead, it only uses selection and a proposed dither creeping mutation operator. The creeping mutation rate in the proposed dither creeping mutation operator is randomly generated in a range throughout a GA run, rather than being set to a fixed value. In addition, the dither mutation rate is applied at an individual chromosome level rather than at the generation level. The dither creeping mutation probability is set to take values from a small range that is centered about $1/ND$ (where $ND$ = number of decision variables of the optimization problem being considered). This is motivated by the fact that a mutation probability of approximately $1/ND$ previously has been demonstrated to be an effective value and is commonly used for the GA. Two case studies are used to investigate the effectiveness of the proposed CMBGA. An objective of this paper is to compare the performance of the proposed CMBGA with four other GA variants and other published results. The results show that the proposed CMBGA exhibits considerable improvement over the considered GA variants, and comparable performance with respect to other previously published results. Two big advantages of the CMBGA are its simplicity and the fact that it requires the tuning of fewer parameters compared with other GA variants.