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The high pressure of water distribution networks (WDNs) increases leakage, bursts, and water consumption. Pressure-reducing valves (PRVs) are widely used in WDNs because of their flexibility and effectiveness in pressure management. This paper studies pressure management by locating and adjusting PRVs in WDNs, considering the proper objective function and the suitable optimization algorithm. The single-objective algorithms and the EPANET hydraulic simulator in MATLAB programming are combined to manage the pressure. Initially, the proposed method improved by selecting the optimal optimization algorithm and objective function. Both fixed outlets and time-modulated modes of PRVs for two ZJ and Bellingham WDNs as case studies were used. Considering the cost, reliability, hydraulic performance, and leakage, the results were then analyzed and compared by changing the number, location, and adjustment of each PRV. The outcomes show the effect of choosing the best algorithms and cost functions on the results. The results also show that this method increases the reliability and controls the nodal pressure to a desired level in WDNs. Practical Applications: Leakages in water distribution networks (WDNs) are problematic for utilities and other governmental agencies. Pressure management in WDNs is inevitable to reduce the rate of bursts and leaks. Using fixed- and time-modulated pressure-reducing valves (PRVs) is essential for smart pressure management in WDNs. This study presented a method for positioning and adjusting PRVs in WDNs. The proposed method was applied to ZJ and Bellingham WDNs by choosing the best optimization algorithm and objective functions. The grey wolf optimizer (GWO) algorithm is proposed as one of the best algorithms for this subject. The presented method tries to provide the best plan for pressure management by examining the cost, leakage, and reliability. The results show that the presented method effectively increases reliability and reduces leakage in WDNs. [ABSTRACT FROM AUTHOR]

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers. [ABSTRACT FROM AUTHOR]

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 (where = number of decision variables of the optimization problem being considered). This is motivated by the fact that a mutation probability of approximately 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. [ABSTRACT FROM AUTHOR]

This paper reports the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multiobjective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives, as follows: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age, and (6) fire-fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This paper demonstrates that a many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision-making in the WDS design process. [ABSTRACT FROM AUTHOR]

Considerable research has been carried out on the optimization of water distribution systems (WDSs) over the last three decades. In previous research, attention has mainly focused on the minimization of cost, due to the high expenditure associated with the construction and maintenance of such systems. However, the impacts of WDSs on the environment usually have not been considered adequately. The recent increasing awareness of sustainability and climate change, especially global warming, has led to research where greenhouse gas (GHG) emissions are considered. In the study described in this paper a multiobjective genetic algorithm for WDS optimization has been used as an explorative tool to investigate the trade-offs between the traditional economic objective of minimizing costs and an additional environmental objective of minimizing GHG emissions. The impacts of minimizing GHG emissions on the results of WDS optimization have been explored for a case study in this paper. The results indicate that the inclusion of GHG emission minimization as one of the objectives results in significant trade-offs between the economic and environmental objectives. Furthermore, a sensitivity analysis has been conducted by using different discount rates in a present value analysis for computing both ongoing costs and GHG emissions. The results obtained show that the Pareto-optimal front is very sensitive to the discount rates used. As a result, the selection of discount rates has a significant impact on final decision making. [ABSTRACT FROM AUTHOR]

In recent years, pressure management based on district metering areas (DMAs) has played an irreplaceable role in background leakage reduction in water distribution networks (WDNs). To further improve DMA management, this paper describes a novel sectorization method to optimize the reduction of background leakage. First, the network trunk mains are optimally selected using a proposed evaluation index which contains three reference indicators and the resulting isolated regions are identified through graph theory algorithm. Meanwhile, a modified community detection algorithm is adopted to partition the oversized regions considering both elevation uniformity within DMAs and demand uniformity across DMAs. Then the optimal layout of flow meters (i.e., inlet pipes) and gate valves is achieved by solving a two-objective optimization for minimizing the number of flow meters and cumulative pressure differences compared with critical points in each DMA. Finally, pressure-reducing valves (PRVs) are installed in every inlet pipe and optimized to reduce leakage. The overall procedure is applied to a medium-sized network. The results reveal that the proposed sectorization methodology combined with pressure management can efficiently reduce leakage by controlling excess hydraulic capacity. [ABSTRACT FROM AUTHOR]

Leaks in water pipelines can create many problems, such as financial loss, environmental pollution, and public health hazards. Previous scholars and engineers have researched leak detection and localization methods for water-filled pipe systems, which have proven to be effective in pipe fault detection, even if under external noise. However, the performance of these techniques is unavoidably affected by the noise from the pipe, which includes branch noise, and has motivated researchers to explore more high-performance technologies further. For this purpose, this paper mainly focuses on the problem of locating leaks in a branch water pipe system. Then, a novel blind deconvolution algorithm, called the complex-efficient FastICA (C-EFastICA) algorithm, is proposed to extract the original leak signal from the mixed leak acoustic signal. Unlike the other complex field blind deconvolution methods, the proposed C-EFastICA can adaptively select the nonlinear function g to establish the cost function and iterative learning rules according to the different generalized Gaussian characteristics, which makes the algorithm more accurate in decomposing mixed leak signals in a complex field. The experiment results show that the C-EFastICA is faster than the classical complex FastICA (C-FastICA) algorithm. The relative precision of the leak localization reached approximately 88% under the interference of branch noise. [ABSTRACT FROM AUTHOR]

Efficiency of hydraulic solvers for the simulation of flows and pressures in water distribution systems (WDSs) is very important, especially in the context of optimization and risk analysis problems, where the hydraulic simulation has to be repeated many times. Among the methods used for hydraulic solvers, the most prominent nowadays is the global gradient algorithm (GGA), based on a hybrid node-loop formulation. Previously, another method based just on loop flow equations was proposed, which presents the advantage that it leads to a system matrix that is in most cases much smaller than in the GGA method, but has also some disadvantages, mainly a less sparse system matrix and the fact that introducing some types of valves requires the redefinition of the set of network loops initially defined. The contribution of this paper is to present solutions for overcoming the mentioned disadvantages of the method based on loop flow equations. In particular, efficient procedures are shown for selecting the network loops so as to achieve a highly sparse matrix and methods are presented to incorporate check valves and automatic control valves while avoiding the need to redefine the loops initially selected. [ABSTRACT FROM AUTHOR]

To enable a water distribution system (WDS) model to yield predictions with a reasonably good match to measurements, pipe resistance coefficients (PRCs) of the model often need to be calibrated. The majority of methods developed recently solve the PRCs' calibration problem using evolutionary algorithms. The common feature of these methods is to perform the forward computation (i.e., solve the traditional hydraulic equation of networks) repeatedly to search optimal solutions. This paper presents a direct inversion algorithm for PRC calibration of WDSs, which has an identical framework with a global gradient algorithm (GGA) that has been proved to be the most effective method to solve networks and adopted by EPANET. Therefore, all advantages of GGA can be extended in terms of high computational efficiency and convergence, as well as being easy to understand and program. In addition, under the proposed framework, uncertainties propagated from measurement noises, nodal demand uncertainties, and model simplification errors to the estimates can be quantified individually or simultaneously in a flexible manner, enabling the identification of uncertainties source, as well as which kind of effort is preferred to reduce them. Three networks are applied to validate the proposed algorithm. Encouraging results achieved demonstrate that the proposed algorithm is valid for the calibration and uncertainty qualification of PRCs, as well as the uncertainty source identification. [ABSTRACT FROM AUTHOR]

This paper presents a case study on the application of a dynamic framework for the intelligent control of flooding in the Boise River system in Idaho. This framework couples a robust and numerically efficient hydraulic routing approach with the popular multi-objective nondominated sorting genetic algorithm II (NSGA-II). The novelty of this framework is that it allows for controlled flooding when the conveyance capacity of the river system is exceeded or is about to exceed. Controlled flooding is based on weight factors assigned to each reach of the system, depending on the amount of damage that would occur, should a flood occur. For example, an urban setting would receive a higher weight factor than a rural or agricultural area. The weight factor for a reach does not need to be constant as it can be made a function of the flooding volume (or water stage) in the reach. The optimization algorithm minimizes flood damage by favoring low-weighted floodplain areas (e.g., rural areas) rather than high-weighted areas (e.g., urban areas) for the overbank flows. The proposed framework has the potential to improve water management and use of flood-prone areas in river systems, especially of those systems subjected to frequent flooding. This work is part of a long-term project that aims to develop a reservoir operation model that combines short-term objectives (e.g., flooding) and long-term objectives (e.g., hydropower, irrigation, water supply). The scope of this first paper is limited to the application of the proposed framework to flood control. Results for the Boise River system show a promising outcome in the application of this framework for flood control. [ABSTRACT FROM AUTHOR]

Optimal design of water distribution networks (WDN) involves an evaluation of both consumers' pressure benefits and investors' economic objectives. The aforementioned objectives often conflict, so finding the optimal solution for one of those objectives reduces the other objective's utility. In such situations, there are many nondominated solutions, each solution denoting an alternative that cannot be preferred over another in terms of both objectives. Thus, an appropriate alternative to fulfill both objectives and satisfy decision makers' criteria and meet the design purposes within a desirable range necessitates the use of bargaining models that are called conflict-resolution models. This paper considers two urban WDN optimization design problems having different objectives, including initial costs and hydraulic performance improvement of the network by satisfying given hydraulic constraints. First, a set of alternatives are drawn out by a fast messy genetic algorithm (FMGA), then the appropriate alternative design is achieved by using Nash's and Young's bargaining models. Thus, the methodology presented in this paper can be employed by designers of WDNs to simultaneously consider the utilities of both consumers and investors that are the main beneficiaries of such infrastructures. Results show that in almost all the alternatives obtained by the proposed methodology, at least 80% of the most probable utility of both beneficiaries is obtained at the same time, which indirectly indicates the low vulnerability of the design alternative by the considered methodology in meeting the goals of each beneficiary. Moreover, results also indicate that when using the same utility functions, decision points obtained from the two models coincide. [ABSTRACT FROM AUTHOR]

Streams and their associated biological communities are among our most valuable natural resources. Humans rely on the environmental services provided by streams in a myriad of ways. However, in some areas, excessive groundwater pumping exacerbates the already critical pressure on streamflow and must be managed through effective planning. Based on economic and hydrogeological concepts, this study estimates the quantity of streamflow depletion that is attributable to groundwater pumping and the negative impact on the socioeconomic system if groundwater pumping must be constrained to restore streamflow. The primary objective of this paper is to develop a multiobjective nonlinear optimization model to simulate the tradeoffs between streamflow restoration and economic welfare loss in a Chicago suburban county, McHenry County. The multiobjective optimization was conducted at both county and municipality levels. An evolutionary algorithm, the nondominated sorting genetic algorithm, was used to solve the optimization model and to identify the tradeoff curve (Pareto frontier). Comparing municipal Pareto frontiers shows spatially heterogeneous costs of preserving streamflow through various shadow prices and also the different capacities of restoring streamflow. The results include discussion of the shapes of the Pareto frontier, the sensitivity of the pumping boundary constraints, and return flow coefficients. It is concluded that the multiobjective optimization model provides a useful framework to consider conflicting objectives in a typical environmental management and planning process, and that the findings can help decision-makers and planners to formulate effective groundwater pumping strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The area of systematic identification of contamination sources in water distribution systems is in its infancy and is rapidly growing. The real water distribution network problem poses many challenges that current methods usually assume away to facilitate manageable method development and testing. Current methods may not readily and efficiently address issues, such as multiple sources, unknown contamination types with different reaction kinetics, use of different types of sensors with varying degree of resolution, dynamically varying demand and sensor information, and uncertainty and errors in the data and measurements. With the aim of addressing these imminent challenges, this paper reports the findings of an ongoing research investigation that develops and tests an evolutionary algorithm-based flexible and generic procedure, which is structured within a simulation-optimization paradigm. This paper describes the specific implementation of the method using evolution strategies (ESs), a population-based heuristic global search algorithm. A key component of designing this source characterization method is to define a compact, but comprehensive, solution encoding structure. The new method is constructed using a tree-based encoding design to enable the representation of variable-length decision vectors and a set of associated genetic operators that enable an efficient search. This algorithm is successfully tested and demonstrated to have consistently good performance for several instances of an illustrative water distribution contamination case study. As the ES-based algorithm conducts a probabilistic search, its robustness is tested using multiple random trials, and the method is shown to exhibit a robust behavior. [ABSTRACT FROM AUTHOR]

In recent years, many researchers have tried to predict the behavior of the water distribution systems under pressure-deficient conditions. The root of the problem is that under these conditions the traditional demand-driven analysis will compute heads below the minimum required for outflow to occur physically at some or all of the nodes. The purpose of this paper is to present a novel algorithm for the solution of a water distribution network under pressure-deficient conditions, and is termed the pressure-deficient network algorithm (PDNA). The proposed model progressively introduces a set of artificial reservoirs into the network to initiate nodal flows, with the ultimate replacement of such reservoirs by full demand loads once it has become clear that the nodal flow can be satisfied. The foundation for the solution methodology is established using a series network for ease of discussion. The PDNA is presented in the form for coding into a computer program. For solving the flows in a looped network, the PDNA has to be used with a hydraulic network solver, as manual computation is too time consuming. Using the EPANET 2 hydraulic network solver, the PDNA is applied to both a single-source and a multiple-source network. The results show that the behavior of a water distribution system under pressure-deficient conditions is complex and nonintuitive. [ABSTRACT FROM AUTHOR]

Reservoir systems are essential for water resources management. The application and development of optimization techniques for optimal reservoir operation is therefore a valuable undertaking. This paper presents a modified firefly algorithm (MFA) and applies it to optimally solve reservoir operation problems. Three well-known benchmark multireservoir operation problems are optimized for energy production. The results of the MFA are compared with results obtained with other mathematical programming approaches, such as linear programming (LP), differential dynamic programming (DDP), and discrete DDP (DDDP), the genetic algorithm (GA), the multicolony ant algorithm (MCAA), the honey-bee mating optimization (HBMO) algorithm, the water cycle algorithm (WCA), the bat algorithm (BA), and the biogeography-based optimization (BBO) algorithm. The MFA was found to be more effective than alternative optimization methods in solving the test problems demonstrating its strong potential to tackle multireservoir operation problems. This paper's results indicate that the MFA differed by 0.01 and 0.79% with the LP global optimal solutions of a continuous four-reservoir problem (CFP) and a continuous 10-reservoir problem (CTP), respectively. The objective function of a discrete four-reservoir problem (DFP) obtained with the MFA is equal to the LP's objective function. This paper demonstrates that the MFA is a competitive optimization method with which to solve a variety of reservoir operation problems. [ABSTRACT FROM AUTHOR]

This paper presents a coupled self-adaptive multiobjective differential evolution and network flow algorithm for the optimal operation of complex multipurpose reservoir systems. The developed algorithm (i.e., self-adaptive multiobjective differential evolution) is compared to nondominated sorting genetic algorithm II using a set of common test problems and a real-world case study. An out-of- kilter method for minimal-cost flow problems is used to optimize the water resource system from self-adaptive multiobjective differential evolution inputs driven by the evolutionary process. Self-adaptive multiobjective differential evolution is then used to evaluate objective functions based on the outputs from out-of-kilter algorithm and the process continues until the stop criterion is met. The advantages of the proposed approach include (1) flexible evolutionary algorithms for solving highly complex objective function, and (2) efficient network flow method for dealing with large and highly constrained problems. The case study includes one part of a complex water supply system located in southwestern Brazil that provides water for almost 20 million people in Sao Paulo metropolitan area. The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. The coupled model is applied to the case study using one inflow scenario representing a drought period with inflows below historical average. Multiobjective analyses are performed by comparing two pairs of objective functions, as follows: (1) minimization of demand shortage versus minimization of pumping cost, and (2) minimization of demand shortage versus minimization of the deviation from the water quality standards. The problem constraints include reservoir capacity, capacity of tunnels, channel flow limitations, and minimum downstream release for all reservoirs within the system. The proposed coupled model (self-adaptive multiobjective differential evolution and out-of-kilter) is outperforming both pure self-adaptive multiobjective differential evolution and nondominated sorting genetic algorithm II, as it requires significantly smaller number of generations to derive the Pareto front. In addition, the proposed approach is capable of handling larger problems without major computational burden. The coupled model and self-adaptive multiobjective differential evolution also converge closer to, and provide better coverage of the true Pareto front than, nondominated sorting genetic algorithm II. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The progressive optimality algorithm (POA) is commonly used to identify optimal hydropower operation schedules in China. However, POA may not converge within a reasonable time for large and complex problems because its computational burden grows exponentially with the expansion of system scale. In order to effectively alleviate the dimensionality problem of POA, an improved POAvariant called orthogonal progressive optimality algorithm (OPOA) is introduced in this paper. In the OPOA, an orthogonal experimental design is used to replace the exhaustive combinatorial evaluation at each POA two-stage subproblem. The theoretical analysis shows that POA and OPOA have exponential and approximately polynomial growth in computational complexity, respectively. The proposed method is applied to a large-scale multireservoir system located on the Wu River in China. The results indicate that, compared with POA, OPOA can remarkably enhance the computing efficiency in different cases, showing its practicability and feasibility for multireservoir system operation. [ABSTRACT FROM AUTHOR]

This work presents a postoptimization methodology for refining the solutions found by adaptive search algorithms used in the design of large water distribution networks. The approach uses two heuristics to search for an optimal combination of pipes that, after a reduction of their diameters, will maximize cost savings while continuing to meet design constraints. Adaptive search methods are often used to design urban water distribution networks when the number of pipes in the network is insignificant. For complex, real-world networks, however, such methods are computationally demanding, and they have difficulty finding near-global optima. To identify a solution as close to the global optimum (and in which no pipe can be reduced without violating pressure constraint), requires a high-speed computer potentially running for a long time and also probably some good fortune. The postoptimization approach presented in this paper is shown to be an efficient complement to heuristic search algorithms used in the design of real-world networks. In a network created with the aid of a genetic algorithm, the proposed heuristics found that 4.37% of the pipes with a diameter greater than the minimum could be further reduced without causing hydraulic failure. [ABSTRACT FROM AUTHOR]

Optimal design of tunnels and surge tanks is a major concern in the implementation and operation of large hydropower plants, particularly those in which tunnels are long and water hammer is likely. In this paper, flow simulation and an optimization process have been linked in an iterative manner to develop a general model for the optimal design of hydropower tunnels and surge tanks for the emergency condition of operation (maximum surge pressure). The governing equations for the analysis of transient flow in conduits are solved using a finite difference scheme. Furthermore, a genetic algorithm (GA) optimization technique is utilized to select the optimal diameter for the headrace tunnel, penstocks, and surge tanks. The benefit-cost ratio is considered as the objective function in the optimization analysis. The proposed method was used to design diameters for Marun hydropower tunnels (an in-operation large hydropower dam located in southwest Iran). The results were compared with results from a nonlinear optimization technique. They revealed considerable savings in construction cost, attributable to the application of GA. The calculated pressures across the tunnels with and without surge tank agree with valve closure theory and previous studies. The model is able to optimize diameters for a branching system of tunnels and penstocks with surge tanks. [ABSTRACT FROM AUTHOR]

This paper discusses the development of a software-aided approach to best management practice (BMP) selection. Supported with input from a geographic information system (GIS), this software provides an algorithmic, mathematically based alternative to the inherently subjective approaches currently used to choose BMPs for management of storm-water runoff. The BMP selection software was applied to a study site located in the town of Blacksburg, Virginia. The software was utilized to identify BMP alternatives for a distributed storm-water management approach on the site. When compared with a base model with no runoff management, the traditional, detention-based management approach yielded 13.0% greater runoff rate reduction than did the distributed model. By contrast, the distributed storm-water management approach greatly outperformed the detention-based approach in removing pollutants from surface runoff. Although both runoff management strategies removed a significant portion of suspended sediment from the demonstration site's runoff, the distributed model achieved a nearly 40% greater removal efficiency. Similarly, although both runoff management approaches remove a significant portion of total phosphorus from the demonstration site's runoff, the distributed model achieved a 38% greater removal efficiency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The suitability of genetic algorithms (GAs) for the optimization of water distribution systems (WDSs) has been demonstrated extensively. However, despite many years of application in many different fields, the selection of the GA parameters remains a difficult and time consuming task. In this paper, two methodologies that do not require trial-and-error GA parameter calibration have been tested on a WDS optimization problem to determine their suitability for application in the water resources field and to assess their ability in locating near-optimal solutions. The results indicate that both approaches located solutions that were significantly better than a GA using typical parameter values, while the methodology based on convergence of the GA population located the best solutions overall. This method can be easily applied to assist GA users in identifying suitable GA parameters without requiring a time consuming trial-and-error approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Water distribution systems are one of the most important infrastructures in urban areas. The objective of every urban water distribution system is to deliver enough water of acceptable quality with adequate pressure to different demand points. There are many interruptions and occasional disasters that impact the performance of the water distribution system, some of which could be quite devastating. The most common disasters are main breaks that may cause considerable water losses and bring up the system to partial or complete shutdown. Evaluation of the state of the system’s readiness helps managers make better decisions to prevent disasters and respond better to emergencies. In this paper, the state of the system’s readiness in dealing with disasters has been quantified using a hybrid index called the system readiness index. This index is developed based on the combined effects of three system performance indexes, namely reliability, resiliency, and vulnerability. They are combined through hydraulic characteristics of the network at some critical nodes of the water distribution system, using a neural network model. Different failure scenarios are defined to evaluate the system performance and to analyze the systems interruption. A Bayesian approach for updating the probability of failure is used to incorporate the new information on the state of the system. The proposed algorithm is applied to a part of the water distribution system of the Tehran metropolitan area in Iran. The results show the significant value of the proposed algorithm in helping the decision maker to improve the system’s performance and develop contingency plans when faced with disasters. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimal pressure management in water distribution systems through the introduction and regulation of pressure reducing valves. Reduction in pressure is aimed at controlling water leakages which, being in some cases a high proportion of the total volume supplied, are nowadays one of the major concerns for water utilities. The determination of the number, location, and setting of such valves is formulated as a two criteria optimization problem and is solved with multiobjective genetic algorithms. In particular, the first criterion is represented by the minimization of the number of valves, and the second is the minimization of the total leakage in the system, when maintaining the required pressure at each node. The great advantage of the multiobjective approach resides in the fact that, in one run, several trade-off alternatives are found, thus providing the set of the optimal solutions with a different level of compromise between the conflicting objectives. At the same time, data necessary for practical choice and operation of pressure reducing valves may be determined. [ABSTRACT FROM AUTHOR]

This paper describes a model developed for optimizing water supply plans for water customers when a water network is partially operational due to the destruction of water networks by malicious attacks or any catastrophic events. The model uses the information regarding the priority of water customers in emergency situations, their functional sensitivities to water supply, and the dynamic nature of water demand patterns. The solutions generated from the model are water rationing plans which can guarantee at least a short-term water supply to water customers during any 24-h period. A genetic algorithm based program linked with a hydraulic solver (EPANET 2.0) is developed to find optimal water rationing plans. A case study indicates that the proposed model can successfully generate water rationing plans to reduce the consequences while meeting hydraulic constraints in water networks. The model developed in this study is also applicable to extreme water supply shortage situations due to severe drought or nonintentional incidents such as major pump failures and main breaks. [ABSTRACT FROM AUTHOR]

The problem of deploying sensors in a large water distribution network is considered, in order to detect the malicious introduction of contaminants. It is shown that a large class of realistic objective functions—such as reduction of detection time and the population protected from consuming contaminated water—exhibits an important diminishing returns effect called submodularity. The submodularity of these objectives is exploited in order to design efficient placement algorithms with provable performance guarantees. The algorithms presented in this paper do not rely on mixed integer programming, and scale well to networks of arbitrary size. The problem instances considered in the approach presented in this paper are orders of magnitude (a factor of 72) larger than the largest problems solved in the literature. It is shown how the method presented here can be extended to multicriteria optimization, selecting placements robust to sensor failures and optimizing minimax criteria. Extensive empirical evidence on the effectiveness of the method presented in this paper on two benchmark distribution networks, and an actual drinking water distribution system of greater than 21,000 nodes, is presented. [ABSTRACT FROM AUTHOR]

This paper introduces a simple topological approach to systematize the isolation of contaminated areas within the pressure zones of drinking water distribution systems (DWDSs). Assuming optimal location of contaminant detectors and known flow conditions, a heuristic procedure delineates the area to be isolated and identifies the valves to be closed by response teams sent in the field, taking into account a response delay from the time of first detection. As a first step leading to the development of a more comprehensive algorithmic application, the approach was elaborated and validated from a pragmatic perspective using two real-world DWDSs. Depending on each network’s design, configuration, and assumed flow conditions, application of the isolation procedure will result in different isolation strategies (extent of isolated areas, number of required isolation valves). The current approach is based on a set of simplifying assumptions, and needs to be further validated with other networks. The proposed methodology can be used to assess the required emergency response capabilities and even possible network design improvements. [ABSTRACT FROM AUTHOR]

The short-term optimal hydrogeneration planning is a complicated nonlinear constrained optimization problem with water delay time. To overcome the shortcomings of a standard genetic algorithm, this paper proposes a new real-value encoding self-adaptive chaotic genetic algorithm to solve this problem, which designs a new crossover operator in light of probability distribution function and a self-adaptive chaotic mutation operator combined chaotic dynamic character with artificial neural network theory. Constraints can be dealt with by using a simple direct comparison penalty function method without the need of any penalty coefficient. The feasibility of the proposed method is demonstrated for short-term generation scheduling of two test hydrosystems and the test results are compared with those obtained by the standard genetic algorithm in terms of solution quality and convergence characteristic. The simulation results show that the proposed method is capable of obtaining higher quality solutions. [ABSTRACT FROM AUTHOR]

The optimization of looped water distribution networks (WDN) implies achieving the best tradeoff between reliability and cost. Many works have dealt with minimizing the cost, but few have considered both cost and reliability. This paper aims to evaluate the performance of several multiobjective metaheuristics (MOMHs) to optimize the design of looped water distribution networks, taking into consideration two objective functions: Minimizing costs and maximizing the resilience index. The MOMHs used are: Strength Pareto Evolutionary Algorithm 2 (SPEA2), Pareto Archived Evolution Strategy, Pareto Envelope-based Selection Algorithm, Pareto Simulated Annealing, and Multiobjective Simulated Annealing and Tabu Search. These algorithms are tested and compared by applying them to two case studies: The first is a small well-known benchmark network (Hanoi), whereas the second one is a larger looped irrigation network (Balerma). Results of this work show that SPEA2 outperforms the remaining methods in terms of Pareto dominance. [ABSTRACT FROM AUTHOR]

Contemporary heuristic search procedures [e.g., evolutionary algorithms (EAs)] continue to offer increased capabilities for systematic search for a range of water resources and environmental management problems. These problems are often riddled, however, with numerous unquantifiable issues that are important when making decisions, but escape being incorporated in the system model. The mathematically optimal solution to such an incompletely defined model may be found unrealistic or altogether incorrect for the real problem. Optimization procedures could still be made useful if they can be utilized effectively to generate, in addition to the optimal solution, a small number of different alternatives that are near optimal. Alternatives with maximal differences in the decision variable values are expected to perform differently with respect to the unmodeled issues, providing valuable choices when making decisions. Although successful alternative generation procedures have been reported for mathematical programming-based search procedures, they are yet to be explored fully for EAs. This paper describes an extensive investigation of a new EA-based alternatives generation procedure, the evolutionary algorithm to generate alternatives (EAGA). A previously published regional wastewater treatment optimization study is used as a basis for establishing and demonstrating the capabilities of EAGA, and the set of results from the previous study is used as a benchmark for comparing the performance of EAGA. Comparisons of results indicate that EAGA is effective in generating good alternative solutions that perform differently with respect to several unmodeled issues. EAGA is sufficiently flexible to be applied to a wide range of water resources and environmental management problems. Further, EAGA can be applied to any problem that is set up to be solved using an evolutionary algorithm. [ABSTRACT FROM AUTHOR]

A genetic algorithm (GA)-based method for the least-cost design of looped pipe networks for various levels of redundancy is presented in this paper. Redundancy constraints are introduced in the optimization model by considering the number of pipes assumed to be out of service at any one time. Using this approach, trade-off relationships between cost and redundancy are developed. The GA-based approach is computationally intensive, and implementations on a custom fault-tolerant distributed computing framework, called Vitri, are used to satisfy the computational requirements. The design methodology is applied to two water distribution networks of different sizes, and a comparison of the performance of the distributed GAs for the design problems is also presented. We conclude that a GA-based approach to obtaining cost-effective, redundant solutions for the least-cost design of looped pipe networks can be effectively used on a heterogeneous network of nondedicated workstations. [ABSTRACT FROM AUTHOR]

Due to inherent variability in instantaneous water consumption levels, values of demands at nodes in a water distribution system remain one of the major sources of uncertainty in the network design process. Uncertainty in demand leads to uncertainty in head at the nodes, which, in turn, affects the system performance and has to be taken into account when designing new water distribution systems or extending/rehabilitating existing ones. One approach to dealing with this difficulty is to formulate and solve the stochastic optimization problem providing a robust, cost-effective solution. However, stochastic formulation usually requires Monte Carlo simulation, which involves calculation of a large number of state estimates, even for relatively simple networks. This renders the approach intractable when combined with heuristic adaptive search techniques, such as genetic algorithms (GAs) or simulated annealing. These methodologies require the fitness function to be evaluated for thousands of possible network configurations in the course of the search process. In this paper a new approach to quantifying the influence of demand uncertainty on nodal heads is proposed. The original stochastic model is reformulated as a deterministic one, which uses standard deviation as a natural measure of variability. Such an approach allows the use of effective numerical methods to quantify the influence of uncertainty on the robustness of water distribution system solutions. The deterministic equivalent is then coupled with an efficient GA solver to find robust and economic solutions. The proposed methodology was tested on the New York tunnels and Anytown problems. A number of low cost network solutions were found for different levels of reliability and different forms of probability distribution function for demands. The robustness of the solutions found was compared to known solutions for deterministic formulations, whose results were postprocessed using full Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Identifying contaminant sources in groundwater is important for developing effective remediation strategies and identifying responsible parties in a contamination incident. Groundwater source identification problems require solution of an inverse problem. Gradient-based local optimization approaches are among the most popular approaches for solving these inverse problems. While these methods are sometimes appropriate, they are not effective for problems that contain several local minima and for problems where the decision space is highly discontinuous or convoluted. For these types of problems, heuristic global search approaches such as genetic algorithms (GAs) are more effective. But methods such as GAs are inefficient for fine-tuning solutions once a near global minimum is found. For problems that contain several local minima, a hybrid approach starting with a global method and then fine-tuning with a local method may be more attractive, especially if the decision space is reasonably well behaved near the solution. In this paper, we compare several popular optimization methods for solving a simple groundwater source identification problem and show that hybrid GA-local search (GA-LS) approaches are generally more effective than using stand alone versions of each method. Some variants of the GA-LS approaches are then implemented on a parallel supercomputer to solve a more complex three-dimensional problem. [ABSTRACT FROM AUTHOR]

Shuffled Frog Leaping Algorithm (SFLA) is a meta-heuristic for solving discrete optimization problems. Here it is applied to determine optimal discrete pipe sizes for new pipe networks and for network expansions. SFLA is a population based, cooperative search metaphor inspired by natural memetics. The algorithm uses memetic evolution in the form of infection of ideas from one individual to another in a local search. The local search is similar in concept to particle swarm optimization. A shuffling strategy allows for the exchange of information between local searches to move toward a global optimum. This paper summarizes the development of SFLANET, a computer model that links SFLA and the hydraulic simulation software EPANET and its library functions. Application of SFLANET to literature network design problems is then described. Although the algorithm is in its initial stages of development, promising results were obtained. [ABSTRACT FROM AUTHOR]

Replacement of deteriorated water pipes is a capital-intensive activity for utility companies. Replacement planning aims to minimize total costs while maintaining a satisfactory level of service and is usually conducted for individual pipes. Scheduling replacement in groups is seen to be a better method and has the potential to provide benefits such as the reduction of maintenance costs and service interruptions. However, developing group replacement schedules is a complex task and often beyond the ability of a human expert, especially when multiple or conflicting objectives need to be catered for, such as minimization of total costs and service interruptions. This paper describes the development of a novel replacement decision optimization model for group scheduling (RDOM-GS), which enables multiple group-scheduling criteria by integrating new cost functions, a service interruption model, and optimization algorithms into a unified procedure. An industry case study demonstrates that RDOM-GS can improve replacement planning significantly and reduce costs and service interruptions. [ABSTRACT FROM AUTHOR]

The effort to manage irrigation water use through the adoption and implementation of agricultural best management practices (BMPs) is crucial for meeting water bodies' sustainability in water-deficient agricultural areas. This paper presents the development of an efficient decision support tool able to suggest the optimal location for placing high- and low-tech irrigation BMPs, such as deficit irrigation, conveyance efficiency improvement, precision irrigation, and wastewater reuse, and demonstrates its application in the water-scarce Pinios river basin in central Greece. The tool uses the SWAT (soil and water assessment tool) model as the BMP and hydrologic simulator and a multiobjective genetic algorithm, which, based on calculated cost data, optimizes the cost-effectiveness of management schemes across the landscape. From the analysis of the results produced and the optimal river basin BMP configurations under the current and a volumetric water pricing scenario and under a variation of the cost parameters, the study concludes that a number of different BMPs at an affordable implementation cost, along with a tiered water pricing system that could address socioeconomic heterogeneities, would form a sustainable action plan against desertification in the highly water-deficient Pinios basin. The methodology and tool are considered to be easily applicable in other river basins and could be used to assist in more cost-effective implementation of environmental legislation. [ABSTRACT FROM AUTHOR]

A valve-enclosed segment is the basic unit of water distribution networks (WDNs) in reliability analysis and optimal design. Based on the theory of the transitive closure of graphs, this paper presents a new method for identifying the whole segments in large WDNs in a single run. The method clones real valves as fictitious valves, uses valve pairs to simulate valve on-off status, and improves the Warshall algorithm to compute node transitive closure sets to identify all segments at a time. Additionally, redundant valves, endpoint valves, leaf segments, orphan segments, and regular segments are analyzed. One-way valves are considered in an unintended segment analysis. The method is described and discussed in detail by using a small sample network, then applied to a large real network containing 69,815 valves to prove its capability and efficiency. [ABSTRACT FROM AUTHOR]

A fully automated data-driven methodology for the 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 has been recently developed by the authors. This methodology works by simultaneously analyzing the data coming on-line from all the pressure and/or flow sensors deployed in a DMA. It makes synergistic use of several self-learning artificial intelligence (AI) and statistical techniques. These include (1) wavelets for the de-noising of the recorded pressure/flow signals; (2) artificial neural networks (ANNs) for the short-term forecasting of pressure/flow signal values; (3) statistical process control (SPC) techniques for the short-term and long-term analysis of the burst/other event-induced pressure/flow variations; and (4) Bayesian inference systems (BISs) for inferring the probability that a pipe burst/other event has occurred in the DMA being studied, raising the corresponding detection alarms, and provide information useful for performing event diagnosis. This paper focuses on the (re)calibration of the above detection methodology with the aim of improving the forecasting performances of the ANN models and the classification performances of the BIS used to raise the detection alarms (i.e., DMA-level BIS). This is achieved by using (1) an Evolutionary Algorithm optimization strategy for selecting the best ANN input structures and related parameter values to be used for training the ANN models, and (2) an Expectation Maximization strategy for (re)calibrating the values in the conditional probability tables (CPTs) of the DMA-level BIS. The (re)calibration procedure is tested on a case study involving several DMAs in the U.K. with real-life pipe bursts/other events, engineered pipe burst events (i.e., simulated by opening fire hydrants), and synthetic pipe burst events (i.e., simulated by arbitrarily adding 'burst flows' to an actual flow signal). The results obtained illustrate that the new (re)calibration procedure improves the performance of the event detection methodology in terms of increased detection speed and reliability. [ABSTRACT FROM AUTHOR]

In recent years, a number of evolutionary algorithms have been proposed for optimizing the design and operation of water distribution systems (WDSs). These evolutionary algorithms include genetic algorithms, ant colony optimization, particle swarm optimization, the shuffled leaping frog algorithm, and differential evolution. Although there have been some comparisons made of the performance of the various algorithms, very few of these comparisons have been carried out in a completely rigorous manner. The main aim of this paper is to introduce a methodology for the rigorous comparison of various algorithms for the optimum design of water distribution systems. The methodology involves comparing the various algorithms in terms of (1) the best solution obtained; (2) the speed of convergence; and (3) the spread and consistency of the solutions obtained over a number of random starting seeds and numbers of evaluations. As a demonstration of the methodology, the techniques of genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE) are applied to two frequently used WDS case studies, namely the New York Tunnels and Hanoi water networks. In addition, the techniques are applied to a real-size water distribution system consisting of 476 pipes. The results obtained show that the algorithm performances depend on the specific problem and the number of function evaluations allowed. Moreover, it is shown that correct calibration is an essential phase for a fair comparison of evolutionary algorithms. In fact, the best parameters are a function of the problem characteristics, of the objective function and of the variants in the algorithm operators. Therefore the adoption of configurations tested on slightly different versions of the algorithms can lead to quite different results. [ABSTRACT FROM AUTHOR]

The ability to monitor the flow and how water transforms throughout water networks would greatly improve the management of those distribution systems. The sensor placement problem attempts to find the locations of monitoring devices that would optimally observe water quality and protect consumers from accidents and intrusions of contaminants. In some related critical scenarios, the absence of information about possible contamination events, including knowledge of the injection sources, contaminant types or mass, time of pollution, and the variability of water network input parameters, such as the nodal demands and the pipe roughness coefficients, raises several challenges that must be addressed by the sensor network design process. In this paper, the authors describe a stochastic parameterization and analysis of uncertainty for the design of sensor networks aimed at maximizing the probability of detection of accidents and intrusions in water distribution networks. A challenge with such an approach, in particular, when applied to large urban water networks is the size of the ensuing computational model and the associated numerical optimization problem. This problem is compounded in the presence of uncertainty, where the need to deal with a large number of statistically plausible scenarios underscores the need for efficient yet credible algorithms for addressing the sensor placement issue. In addition to modeling aspects, the authors also address a challenge associated with the computational feasibility and performance of relevant numerical algorithms. Specifically, and through the use of submodular cost functions, the authors are able to solve the optimization problem with a greedy algorithm, yielding sufficient computational performance that allows for the description of stochastic water demands in the context of Monte Carlo simulations. Imperfect sensors are further accounted for while maintaining submodularity. The results show that the optimal sensor network layout is highly dependent on whether uncertainty in the stochastic parameters has been introduced and whether individual sensors can fail or not. [ABSTRACT FROM AUTHOR]

Diminishing marginal utility is an important characteristic of water resources systems. With the assumption of diminishing marginal utility (i.e., concavity) of reservoir utility functions, this paper derives a monotonic relationship between reservoir storage and optimal release decision under both deterministic and stochastic conditions, and proposes an algorithm to improve the computational efficiency of both deterministic dynamic programming (DP) and stochastic dynamic programming (SDP) for reservoir operation with concave objective functions. The results from a real-world case study show that the improved DP and SDP exhibit higher computational efficiency than conventional DP and SDP. The computation complexity of the improved DP and SDP is (order of , the number of state discretization) compared to with conventional DP and SDP. [ABSTRACT FROM AUTHOR]

This paper proposes a modified genetic algorithm (GA) for optimization of water distribution systems. A method of dynamically expanding the pipe-choice-table selections and reducing the number of decision variables is introduced, which occurs during a GA run. On the basis of the progressive selection, an initially reduced choice table for each decision variable is allowed to dynamically expand, and then the number of decision variables is gradually reduced. This process enables the GA search to concentrate on promising regions of the search space. The dynamically expanding choice-table genetic algorithm (GADECT) has been applied to a benchmark case study, the New York Tunnels Problem. The results obtained show that the GADECT yields a superior performance in terms of solution quality and computational efficiency. [ABSTRACT FROM AUTHOR]

Computational cost is a critical issue for large-scale water-resource optimization under uncertainty, since time-intensive Monte Carlo simulations are often required to evaluate over multiple parameter realizations. This paper presents an efficient approach for replacing most Monte Carlo simulations with surrogate models within a noisy genetic algorithm (GA). The surrogates are trained to predict the posterior expectations online on the basis of stochastic decision theory, using Monte Carlo simulation results created during the GA run. The surrogates, which in this application are neural networks, are adaptively updated to improve their prediction performance as the search progresses. A Latin hypercube sampling method is used to efficiently sample parameters for the Monte Carlo simulation, and the sampling results are archived so that the estimate of posterior expectation can be iteratively improved in an efficient manner. In addition, the GA is modified to incorporate hypothesis tests in its selection operator to account for sampling noise. The method is applied to a field-scale groundwater remediation design case study, whereas the primary source of uncertainty stems from hydraulic conductivity values in the aquifers. The results show that the method identified more reliable and cost-effective solutions with 86-90% less computational effort than the purely physically based noisy GA approach. [ABSTRACT FROM AUTHOR]

This paper describes a simulation-optimization model aimed at helping water utilities determine the best way to operate large-scale multisource water-supply systems. The operation of water systems is optimized taking into account the principal planning objectives defined for interventions that include reducing operating costs, satisfying demand, delivering water of appropriate quality, and not prompting the use of emergency sources. The model allows that these planning objectives may not be completely fulfilled in critical circumstances (e.g., droughts) when goals have to be prioritized by the water utilities. The model is a highly nonlinear programming problem and is solved with the general algebraic modeling system (GAMS), using the MINOS algorithm (GAMS/MINOS). The application of the model to the multimunicipal urban water-supply system of the Algarve shows its capabilities for optimizing the operation considering economic and noneconomic goals included in the objective function and to cope with future shortages in critical circumstances by improving the conjunctive use of the different water sources with interannual planning time horizons. [ABSTRACT FROM AUTHOR]

Contamination source identification involves the characterization of the contaminant source based on observations that stream from a set of sensors in a water distribution system (WDS). The streaming data can be processed adaptively to provide an estimate of the source characteristics at any time once the contamination event is detected. In this paper, an adaptive dynamic optimization technique (ADOPT) is proposed for providing a real-time response to a contamination event. A new multiple population-based search that uses an evolutionary algorithm (EA) is investigated. To address nonuniqueness in the initial stages of the search and prevent premature convergence of the EA to an incorrect solution, the multiple populations are designed to maintain a set of alternative solutions that represent various nonunique solutions. As more observations are added, the EA solutions not only migrate to better solution states but the number of solutions decreases as the degree of nonuniqueness diminishes. This new algorithm adaptively converges to the solutions that best match the available observations. The use of the developed method is demonstrated for two WDS networks. [ABSTRACT FROM AUTHOR]

Reservoir flood control operation (RFCO) is a large scale multiobjective problem with complex constraints that require powerful algorithms to solve it. As a new metaheuristic evolutionary algorithm, shuffled frog leaping algorithm (SFLA) has the potential ability to solve multiobjective optimization problems because of its group evolution characteristic. In this paper, we present a novel multiobjective shuffled frog leaping algorithm (MOSFLA), which incorporates an archiving strategy based on self-adaptive niche method to maintain the nondominated solutions, and improves the memetic evolution process of SFLA to adapt to the multiobjective optimization problem. The numerical experiments of five Zitzler-Deb-Thiele functions indicate that MOSFLA yields better-spread solutions and converges closer to the true Pareto frontier than non-denominated sorting genetic algorithm (NGSA)-II and SPEA2. Furthermore, MOSFLA is applied to solve RFCO of the Three Gorges Project, and the results demonstrate that this algorithm can generate a solution set with uniform spread and good convergence for the problems with two conflicting objectives, including minimizing the highest reservoir water level and minimizing the peak flood discharge. Additionally, if compared with dynamic programming and NGSA-II, MOSFLA is verified to be more efficient and competitive, and thus can be provided as a new effective alternative for solving the complex reservoir operation problems. [ABSTRACT FROM AUTHOR]