Journal of Water Resources Planning and Management

During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, including a call to address larger-scale problems that are wrought with uncertainty and an expanded need for cross fertilization and collaboration among our field’s subdisciplines. Evolutionary computation will continue to evolve in the future as we encounter increased problem complexities and uncertainty and as the societal pressure for more innovative and efficient solutions rises.

Optimization of large and hydraulically complex water distribution systems (WDSs) is computationally expensive as simulation models are required to evaluate the performance of solutions to the problem at hand. Metamodels can act as a surrogate or substitute for these simulation models and provide significant speed-ups in the optimization process. The application of metamodels in the field of WDS optimization has been limited to date, and little guidance has been given in terms of constructing metamodels for hydraulically complex systems. While it is relatively straightforward to obtain satisfactory metamodel approximations to simulation models of simple WDSs, this is not necessarily the case for more complex networks. In order to reduce the complexity of the relationship that is to be approximated by the metamodels, a number of factors have to be considered, including the complexity of the hydraulic simulation model, the complexity of the decision space, and the locations at which outputs are required from the hydraulic simulation model. This research presents a systematic methodology for dealing with these factors and demonstrates the effectiveness of the approach by applying it to an actual WDS. A system in Wallan, Victoria, Australia, is selected for demonstration purposes. Four different metamodelling scenarios are presented here. The results show that, for this case study, some skeletonization of the model is required to achieve suitably accurate metamodels. The optimization results show a reduction in the average daily pumping costs from $457 to $363; a saving of 21%. The net present value (NPV) over 25 years is used as the objective function, which includes both pumping and chlorine costs. The current operating regime corresponds to an NPV of $1.56 million, while the optimized solution has an NPV of $1.34 million; a saving of 14%. In addition to these economic benefits, the optimized solution achieves adequate disinfection throughout the system, whereas the current operating regime results in deficits in chlorine residuals at several locations in the system.

In case of contamination intrusion in water distribution systems, water quality sensor data can be used to determine the location and time of the contamination source. One approach to contamination source identification is finding the source location that minimizes the difference between modeled and measured water quality. However, this is an inherently ill-posed mathematical problem, due to the shortage of measurements compared to source parameters, and regularization methods are required to force identification of a unique solution. 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. Since sensors cannot detect the quantitative concentration of a contaminant, this method only requires a binary sensor status over time. A particle backtracking algorithm is used to identify the water flow paths and travel times leading to each sensor measurement. Those locations and times that are connected to positive sensor measurements, but are not connected to negative measurements, are the possible sources, assuming no false positive/negative readings and an accurate hydraulic model. 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.

In this paper, using a stylized mathematical simulation model, the participation of farmers in spot and options markets is analyzed in a two-period setting. Farmers assess the benefits from entering one of the two markets based upon anticipated water supplies in a subsequent period and relative expected profits in water and agricultural markets. The supply of water for urban use is a function of uncertain weather conditions and water demands for agricultural uses. The analysis offers insights on water market participation by deriving conditions that favor spot or options market participation. The findings highlight the importance of market size, agricultural profitability, and other parameters in predicting the potential success of spot and options market formation.

Historically, a water distribution system’s (WDS) hydraulic performance has been the primary operational concern. Over the past two decades, however, more attention has been paid to water quality behavior in WDS and today, water quality level is an equally important issue for many water utilities. In most cases, maintaining disinfectant levels is usually of interest to avoid the bacteria regrowth and to protect against the potential cross-contamination events. However, disinfectants, such as chlorine, decay over time and produce potentially harmful disinfectant by-products when they react with organic material in the water. Therefore, maintaining a minimum chlorine residual requirement throughout the WDS is a complex but important task. When online booster disinfection is combined with source disinfection, it has been shown that the total chlorine dosage can be reduced while maintaining minimum chlorine residuals across the system. Here, optimal valve operation has been combined with booster disinfection to improve the system water quality. Valves can be operated to alter the flow distribution in the network; prevent the isolation of water; and direct disinfectant laden water to locations where it is needed. A real-time optimal valve operation and booster disinfection problem is formulated as a single objective optimization model. The objective is to minimize chlorine injection mass at sources or to minimize excessive chlorine concentrations at withdrawal points while maintaining minimum chlorine concentrations and pressures throughout the system. The problem is solved using a genetic algorithm (GA). The application to a medium-sized WDS shows that optimal operation of existing valves combined with booster disinfection can improve water quality while requiring lower chlorine doses and resulting in little significant pressure reduction. Also, real-time operations can adapt to the temporal and spatial variations of system demands.

The valuation of ecosystem services such as drinking water provision is of growing national and international interest. The cost of drinking water provision is directly linked to the quality of its raw water input, which is itself affected by upstream land use patterns. This analysis employs the benefit transfer method to quantify the economic benefits of water quality improvements for drinking water production in the Neuse River Basin in North Carolina. Two benefit transfer approaches, value transfer and function transfer, are implemented by combining the results of four previously published studies with data collected from eight Neuse Basin water treatment plants. The mean net present value of the cost reduction estimates for the entire Neuse Basin ranged from $2.7 million to $16.6 million for a 30% improvement in water quality over a 30-year period. The value-transfer approach tended to produce larger expected benefits than the function-transfer approach, but both approaches produced similar results despite the differences in their methodologies, time frames, study sites, and assumptions.

A two-step approach is proposed to assist forensic investigation of possible source locations following a contaminant detection in drinking water systems. Typically this identification problem is ill posed as it has more unknowns than observations. First, linear algebra is employed to rule out potential contaminant injections. Second, an entropic-based Bayesian inversion technique, the minimum relative entropy method, solves for the remaining variables. This formulation allows for the less committed prior distribution with respect to unknown information and can include model uncertainties and measurement errors. The solution is a space-time contaminant concentration probability density function accounting for the various possible injections that may be the cause of the observed data. Besides, a probability measure quantifying the odds of being the actual location of contamination is assigned to each potential source. Effectiveness and features of the method are studied on two example networks.

Maintaining a disinfectant residual in water distribution systems (WDSs) is generally considered paramount to ensuring a safe drinking water supply. This objective can be assisted by the use of booster stations to increase disinfectant concentrations throughout the network. However, identifying the appropriate dose at each station is an optimization problem. The aim is to minimize the total mass of disinfectant dosed and reduce the cost of disinfection along with potential taste, odor, or by-product problems, while maintaining a certain minimum residual in the network. The residual present in the water at any location is not only dependent on the amount of disinfectant added to the water, but also the hydraulics of the system and the resulting detention times. A number of previous studies have tackled this optimization problem, however, a review of current literature suggests that in many cases the hydraulics of the system have been held constant, or the WDSs considered were hypothetical systems with relatively few constraints. This study considers the booster disinfection dosing problem, including daily pump scheduling, for a real system in Sydney, Australia. Before the system can be optimized, a representative model is required to ensure useful results, and the many constraints on the daily operation system must be accounted for in the fitness function considered. The results from the optimization study indicate it is necessary to consider the hydraulics as well as the dosing regime in the optimization process, as cycling reservoir levels minimizes detention times, and hence, disinfectant residuals are maintained at the extremities of the network. Also, significant energy cost savings of up to 30% can be made by scheduling the pumping in the system in line with the off-peak electricity costs.

Water management in the Pirapama River Basin in northeastern Brazil is affected by both water quantity and water quality constraints. The region is known for significant sugarcane-based ethanol production—which is the key to the Brazilian economy and expected to grow dramatically under recent changes in climate and energy policies. Sugarcane production in the region goes hand in hand with controlled fertirrigation practices with potentially significant adverse impacts on the environment. To assess sustainable water allocation in the basin, an integrated hydrologic-economic basin model is adapted to study both water quantity and quality aspects. The model results show that incorporating water quality aspects into water allocation decisions leads to a substantial reduction in application of vinasse to sugarcane fields. Embedding highly nonlinear water quality constraints in a basin optimization model is demonstrated to be an effective tool to address the water management problems in rapidly developing biofuel areas of Brazil and elsewhere.

Demand estimation has been solved by using a weighted least-squares (WLS) estimator incorporating field measurements with system simulation model. WLS estimator results are sensitive to spurious measurements caused by supervisory control and data acquisition malfunctions. Estimates using the contaminated measurements are not reliable and bad data should be filtered prior to demand estimation. This study presents a series of statistical methods to detect bad data, identify their locations, and correct the data values. The proposed methods are based on a linear measurement model that linearly relates state variables (nodal demands) to the field measurements (pipe flow rates). Application to a simple hypothetical network using synthetically generated data shows that the method can be successfully used as a preprocessing for single and multiple noninteracting bad data for reliable demand estimation.