Journal of Water Resources Planning and Management

In this study we provide a methodology for the optimal design of water sensor placement in water distribution networks. The optimization algorithm used is based on a simulation-optimization and a single-objective function approach which incorporates multiple factors used in the design of the system. In this sense the proposed model mimics a multiobjective approach and yields the final design without explicitly specifying a preference among the multiple objectives of the problem. A reliability constraint concept is also introduced into the optimization model such that the minimum number of sensors and their optimal placement can be identified in order to satisfy a prespecified reliability criterion for the network. Progressive genetic algorithm approach is used for the solution of the model. The algorithm works on a subset of the complete set of junctions present in the system and the final solution is obtained through the evolution of subdomain sets. The proposed algorithm is applied to the two test networks to assess the selected design. The results of the proposed solution are discussed comparatively with the outcome of other solutions that were submitted to a water distribution systems analysis symposium. These comparisons indicate that the algorithm proposed here is an effective approach in solving this problem.

A water demand end-use model was developed to predict water demand patterns with a small time scale (1 s) and small spatial scale (residence level). The end-use model is based on statistical information of users and end-uses: census data such as the number of people per household and their ages; the frequency of use; duration and flow per water-use event; occurrence over the day for different end-uses such as flushing the toilet, doing the laundry, washing hands, etc. With this approach, water demand patterns can be simulated. The simulation results were compared to measured water demand patterns on attributes such as peak flow and daily total water use, as well as on the shape of the pattern and the frequency distribution of flows and accelerations in flow. The simulation results show a good correspondence to measured water demands. Because the end-use model is based on statistical information rather than flow measurements, the model is transferable to diverse residential areas in different countries. The model can be applied in the design stage (prebuild), in scenario studies, and in water quality distribution network models.

The paucity of readily available demographic, economic, and water consumption data at household levels has limited the application of disaggregate water demand models. This research develops regression-based water demand models capable of forecasting single-family residential water demands within individual census tracts at a bimonthly time-step. The regression models are estimated using 12 years of demographic, weather, economic, and metered bimonthly water consumption data associated with over 100 unique census tracts in Seattle, Washington. In general, the three regression methods perform well in replicating total single-family water consumption in the study region. Two regression models, a fixed effects model and a random effects model, provide better estimates of water demand within individual census tracts. Improved water demand forecasts at the spatial scale of census tracts provide policy makers and planners information useful for managing water resources. These proposed approaches allow examination of spatially distributed demands within systems, identify the value of targeted conservation and infrastructure development, and improve understanding of the variables impacting demand in heterogeneous areas. The coefficient estimates developed in this research are appropriate for use in spatially disaggregate urban simulation models.

When addressing urban water problems, it is no longer adequate to consider issues of water supply, demand, disposal, and reuse independently. Innovative water management strategies and opportunities for water reuse can only be properly evaluated in the context of their interactions with the broader water system. An integrated linear deterministic optimization model is applied to Beirut, Lebanon, to determine the minimum cost configuration of future water supply, wastewater disposal, and reuse options for a semiarid coastal city. Previous urban water system optimization models considered only a single quality of potable water and were thus unable to demonstrate the cost-effectiveness of reclaimed water among all viable options for water supply. Two innovations of our work include incorporation of the entire anthropogenic water cycle including interconnections between supply, demand, disposal, and reuse and modeling of the suitability of nonpotable and potable qualities of water for each demand sector. The optimization model yields surprising insights. For example, after full use of inexpensive conventional sources, nonpotable direct reuse appears to be Beirut’s most cost-effective option for supply of its urban nonpotable and irrigation demands. Our work highlights the importance of modeling the utility of multiple qualities of water in modern water supply planning.

Contamination emergency in water distribution systems is a complex situation where optimal operation becomes important for public health. In case of emergency corrective operational actions for flushing the pollutant out of the network are needed, which have to be fast and accurate. Under such a stressful situation, trial-and-error simulation experiments with the hydrodynamic and water quality models cannot be applied since significant number of model evaluations may be required to identify the optimal solution. This paper presents a methodology for finding sets of operational interventions in a supply network for flushing a contaminant by minimizing the impact on the population. The situation is treated as both single- and multiobjective optimization problem, which is solved by using evolutionary optimization approaches, in combination with the EPANET solver engine. The methodology is tested on a simple imaginary network configuration, as well as on a real case study for the city of Villavicencio in Colombia. The results prove the usefulness of the approach for advising the operators and decision makers.

A cyclic storage (CS) system is defined as physically-integrated and operationally interconnected surface water and groundwater subsystems with full direct interactions between the subsystems. The proposed definition treats surface and subsurface impoundment subsystems as competing and potentially interconnected parallel storage facilities that minimize most of the problems associated with large-scale surface impoundments for water supply purposes. This paper emphasizes on the development and implementation of a hybrid two-stage genetic algorithm (GA)-linear programming (LP) algorithm to optimize the design and operation of a nonlinear, nonconvex, and large-scale semidistributed, CS system in an irrigable area. Performance of the proposed model is tested with a 240-period problem where other approaches failed to locate a feasible solution. For optimal operation of the system, a set of operating rules are developed for the joint utilization of surface and subsurface storage capacities to meet a predefined demand minimizing construction and operation cost over a 20-seasonal planning period. Results show that CS dominates a noncyclic storage system both in cost and operation flexibility.

The Government of Canada has endorsed a policy to promote water conservation in an attempt to reduce environmental impacts of water use. Nonetheless, total water intake, consumption, and gross water use in the business sector have increased in the period 1981–1996. Water intake intensities, measured in cubic meters of intake water per dollar of production, however, have fallen by 4%. To better understand these changes in intensities, we decompose them into separate composition and technique effects. We show that changes in the composition of business activity have lead to an expansion of water intensive industries. However, changes in techniques and production processes have more than offset this. Overall, water intake intensities have fallen by 7%, after accounting for changes in the relative growth of sectors. These reductions are even stronger when we include recirculated water where technological and process changes have reduced gross water intensities by 18%. At the same time, though, firms have increased their consumption of water so that, on average, consumption intensities rose 17%. Hence, firm level changes in techniques and processes may have increased the impact of economic activity on the environment rather than having reduced it.

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 set of possible contamination events. The suggested methodology is demonstrated using an illustrative example and two water distribution systems example applications.

A methodology is developed based on an optimization model solution for optimal design of groundwater quality monitoring network. Redundancy in monitoring network results in economic and overall inefficiency of the network. Therefore, redundancy reduction is an important issue in the optimal design of a monitoring network. The developed methodology reduces monitoring redundancy. It incorporates the inverse distance weighting method for spatial interpolation of concentration data. The formulated logic-based mixed-integer linear optimization model is solved using the branch-and-bound algorithm. The proposed methodology is tested for a real world problem. Performance of the proposed methodology is evaluated for different scenarios using available historical concentration data. These performance evaluation results show that the proposed methodology performs satisfactorily when compared with other existing methodologies. These results demonstrate the potential applicability of the proposed methodology for groundwater contaminant monitoring network design, while incorporating reduction in redundancy of monitoring locations.

The allocation of water in a multicountry river system necessarily involves conflicting objectives, where increasing water benefits for one country entails losses for other countries. This paper presents the formulation and application of a multiobjective linear programming model, where each objective represents the benefits for a country from using water for agriculture, urban consumption, and energy production, net of conveyance costs. This model is applied to the Euphrates and Tigris River basin and its three riparian countries—Turkey, Syria, and Iraq. The model is used to delineate the set of nondominated solutions (Pareto frontier), and is extended to include political factors and distributional constraints, leading to an allocation of basin water and resulting benefits.

An economic valuation of flood reductions arising from land use changes induced by the Conversion of Cropland to Forest and Grassland Program (CCFGP) in China is detailed in this paper. A distributed hydrologic model is used to analyze and predict changes in the probability of flood occurrences in the Yellow River Basin (YRB) due to revegetation activities under the CCFGP. The expected value of flood reductions due to the avoided flood damage potential is then estimated. Losses brought about by each type of flood in the YRB over time and the probability change in flood occurrences are used in the calculation. Results show that the economic benefits from flood reductions will total China Yuan 362 million during 2010–2020. The inclusion of this economic benefit into a benefit-cost analysis of the CCFGP provides a more comprehensive assessment of the program. Implications for prioritizing management options to mitigate flood disasters in the YRB are drawn.

Cost-effective reduction of water loss is a compelling but challenging task for water utilities. This paper presents a model-based optimization method for leakage detection of water distribution systems. Leakage hotspots are assumed to exist at the model nodes identified. Leakage is represented as pressure-dependent demand simulated as emitter flows at selected model nodes. The leakage detection method is formulated to optimize the leakage node locations and their associated emitter coefficients such that the differences between the model predicted and the field observed values for pressure and flow are minimized. The optimization problem is solved by using a competent genetic algorithm. The leakage detection method is developed as an add-on feature of the optimization-based model calibration tool. This enables engineers to undertake leakage hotspot optimization as an independent task or combine the task with hydraulic model calibration, subject to suitably varied field data. Two case studies are discussed in this paper including an example from literature and a district water system in the United Kingdom. The results obtained illustrate that the optimization model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and the physical measurement limitations from the pressure and flow surveys also referred to as field tests. It is found that the method is effective at being applied for hydraulic conditions that occur in the early hours of the morning, often on water networks with excess design capacity and where hydraulic gradients are slack and loggers may sometimes be working close to their limits of accuracy.

The paper presents a method for the design of water distribution networks with various network elements such as pipes, pumps, and tanks. A steady-state genetic algorithm is used to solve the proposed single objective model. Minimization of total cost, which includes capital cost and energy cost, is considered as the objective. A new approach for tank sizing is proposed, which does not require explicit consideration of some of the operational constraints, such as the limits on maximum and minimum tank levels. Unlike other methods, the one proposed here conducts extended period simulation for every trial solution in the optimization process. To demonstrate the efficacy of the model, a well-known design example, namely the “Anytown” water distribution network is solved. Designs obtained, using the present method, are cheaper than those proposed by other researchers under similar performance conditions but with different tank sizing methods. Comparison of the results obtained with those published in the literature shows the advantage of using the proposed model in obtaining optimal/near optimal solutions.

This paper deals with the computation of short-term (typically $1\text{day}$ ) least-cost pump schedule for water distribution systems with on-off type pumps and well-defined operating points. The objective function is to minimize the overall energy charge for a prescribed water consumption while several constraints are considered, such as storage and source limitations, pump setting bounds, nodal mass balances, and the power demand charge. In this study a neutral search technique with genetic algorithms (GAs) is proposed to solve the least-cost pump schedule problem where neutrality is achieved through objective-fitness mapping instead of representational redundancy. We introduce a fitness assignment technique for groups of individuals where grouping is based on objective similarity. As a consequence two different individuals can be equally fit (belonging to the same group) which implies the neutrality. We compare the neutral search technique to two conventional GA approaches on a hypothetical water distribution system.

This paper reports the results of a numerical investigation aimed at assessing the effectiveness of the real-time control (RTC) of valves in reducing leakage in water supply networks. The investigation was carried out considering the head-driven simulation of a network under successive steady conditions. A literature bench-test case-study was used for the simulations enabling the comparison with other methodologies proposed by previous writers and based on the use of optimization algorithms. The performance of RTC was evaluated in terms of pressure and leakage reduction with respect to uncontrolled conditions in the network. Further elaborations showed the better flexibility of RTC, with respect to optimization algorithms, in adjusting valve regulation under variable daily water demand conditions.