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Bursts in water distribution systems (WDSs) lead to water wastage and negative environmental impacts. Optimizing pressure sensor placement (PSP) for effective burst detection is crucial for prompt response and adverse event mitigation. While many optimization methods are available for this purpose, their resulting PSP strategies often exhibit large variability caused by background noise or metering accuracy, representing low robustness. This consequently hinders the wide application of existing PSP methods for burst detection in WDSs. This study proposes a novel multi‐objective PSP method aimed at maximizing burst detection while minimizing the number of sensors. Within this method, a new threshold metric is introduced to quantify the minimum detectable burst outflow across all pipes. The metric has the key advantage of taking into account the fact that the relative magnitude of the detectable threshold for different pipes is independent of noise, thereby addressing the low robustness issue. The proposed method is applied to three WDSs, and results show that it can consistently achieve robust optimal PSP strategies across diverse noise conditions. Comprehensive comparisons between the proposed method and the other benchmark approaches are conducted using five metrics, including detection performance and layout characteristics. These comparisons reveal the properties of each optimization method and show how detection performance is affected by various placement features. It is anticipated that the proposed method can be useful in engineering practice due to its great robustness in determining the optimal PSP strategies for burst detection in WDSs compared to other alternatives. In water distribution systems (WDSs), bursts can cause water wastage and environmental harm. Strategic pressure sensor placement (PSP) is crucial for prompt burst detection and mitigation. However, existing PSP methods often lack robustness due to background noise or metering inaccuracies, hindering their widespread use. This study introduces a novel multi‐objective PSP method aiming to maximize burst detection while minimizing sensor count. It includes a new threshold metric to quantify minimum detectable burst outflow, independent of noise, addressing the robustness issue. Based on results from three case studies, the proposed method consistently achieves optimal PSP strategies across diverse noise conditions. Comparative analyses with benchmark approaches demonstrate its robustness and effectiveness in detecting bursts. This method can be very useful in engineering as it can provide reliable PSP strategies for WDS burst detection. The proposed method deals with issues of low robustness and inconsistency within pressure sensor placementsFive metrics and three case studies are used for comparison between the proposed method and three existing approachesPlacements with a larger average shortest‐path distance outperform those with an average geographic‐plane distance in detection performance The proposed method deals with issues of low robustness and inconsistency within pressure sensor placements Five metrics and three case studies are used for comparison between the proposed method and three existing approaches Placements with a larger average shortest‐path distance outperform those with an average geographic‐plane distance in detection performance

The digital twins concept enhances modeling and simulation through the integration of real-time data and feedback. This review elucidates the foundational elements of digital twins, covering their concept, entities, domains, and key technologies. More specifically, we investigate the transformative potential of digital twins for the wastewater treatment engineering sector. Our discussion highlights the application of digital twins to wastewater treatment plants (WWTPs) and sewage networks, hardware (i.e., facilities and pipes, sensors for water quality and activated sludge, hydrodynamics, and power consumption), and software (i.e., knowledge-based and data-driven models, mechanistic models, hybrid twins, control methods, and the Internet of Things). Furthermore, two cases are provided, followed by an assessment of current challenges in and perspectives on the application of digital twins in WWTPs. This review serves as an essential primer for wastewater engineers navigating the digital paradigm shift.

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined. Different crowdsourcing‐based methods for acquiring geophysical data are reviewed and categorized across seven domains of geophysicsProject management, data quality, data processing, and privacy issues have hampered wider uptake of crowdsourcing methods for practical applicationsFuture applications of crowdsourcing methods require public education, engagement strategies and incentives, technology developments, and government support

Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real‐time urban flood forecasting. Using existing rainfall intensity measurement technologies, including ground rainfall gauges, ground‐based radar, and satellite remote sensing, it is challenging to obtain estimates of the desired quality and resolution. However, an approach based on processing distributed surveillance camera network imagery through machine learning algorithms to estimate rainfall intensities shows considerable promise. Here, we present a novel approach that first extracts raindrop information from the surveillance camera images (rather than using the raw imagery directly), followed by the use of convolutional neural networks to estimate rainfall intensity from the resulting raindrop information. Evaluation of the approach on 12 rainfall events under both daytime and nighttime conditions shows that generalization ability, and especially nighttime predictive performance, is significantly improved. This represents an important step toward achieving real‐time, high spatiotemporal resolution, measurement of urban rainfall at relatively low cost. A two‐stage algorithm is proposed to provide good quality rainfall intensity estimates using surveillance camera imageryThe generalization capability of the proposed algorithm is demonstrated under different imagery conditionsThis work is an important step toward achieving real‐time, high spatiotemporal resolution of urban rainfall data at low cost A two‐stage algorithm is proposed to provide good quality rainfall intensity estimates using surveillance camera imagery The generalization capability of the proposed algorithm is demonstrated under different imagery conditions This work is an important step toward achieving real‐time, high spatiotemporal resolution of urban rainfall data at low cost

This paper aims to analyze two demand modeling approaches, i.e., top‐down deterministic (TDA) and bottom‐up stochastic (BUA), with particular reference to their impact on the hydraulic modeling of water distribution networks (WDNs). In the applications, the hydraulic modeling is carried out through the extended period simulation (EPS) and unsteady flow modeling (UFM). Taking as benchmark the modeling conditions that are closest to the WDN's real operation (UFM + BUA), the analysis showed that the traditional use of EPS + TDA produces large pressure head and water discharge errors, which can be attenuated only when large temporal steps (up to 1 h in the case study) are used inside EPS. The use of EPS + BUA always yields better results. Indeed, EPS + BUA already gives a good approximation of the WDN's real operation when intermediate temporal steps (larger than 2 min in the case study) are used for the simulation. The trade‐off between consistency of results and computational burden makes EPS + BUA the most suitable tool for real‐time WDN simulation, while benefitting from data acquired through smart meters for the parameterization of demand generation models. Comparison of top‐down deterministic (TDA) and bottom‐up (BUA) stochastic approaches for nodal demand reconstructionLarge temporal steps are recommended to attenuate modeling errors when extended period simulation (EPS) and TDA are usedEPS + BUA gives a good approximation of the WDN's real behavior when intermediate temporal steps, suitable for real‐time simulation, are used

•Spinal artero-venous malformations (sAVM) are rare spinal lesions, and the association with intra-nidal aneurysm is even rarer.•If untreated, spinal artero-venous malformation can lead to permanent disability.•Management of intramedullary artero-venous malformation can be endovascular or microsurgical.•Microsurgical treatment of intramedullary artero-venous malformation could be achieved without the need of myelotomy.•Complex cases of intramedullary AVMs should be managed by a multidisciplinary team.

The establishment of district metered areas (DMAs) is widely recognized as one of the most effective techniques for the optimal management of water distribution networks (WDNs). However, its implementation in real cases is a very challenging task that requires decision aiding. In this work, a comprehensive methodology for the optimal design of DMAs is presented and discussed. The proposed approach consists of a two-objectives optimization problem subjected to a number of constraints related to the topology of the network, financial issues and the network hydraulics. A multi-objective evolutionary algorithm (MOEA) is combined with tools from graph theory for the solution of the problem. The validation of the model and the calibration of the parameters are performed through the application to a well-known example from the literature. Low cost as compared to the budget and a good saving in leakage are obtained. [ABSTRACT FROM AUTHOR]

Most of the contamination source localization methods for water distribution systems (WDSs) assume the availability of accurate water quality models and multi‐parameter online sensors, which are often out of reach of many water utilities. To address this, a novel manual grab‐sampling method (MGSM) is developed to effectively and efficiently locate continuous contamination sources in a WDS using a dynamic and cyclical sampling strategy. The grab samples are collected at a pre‐specified number of hydrants by the corresponding teams followed by laboratory tests. The MGSM optimizes the sampling plan at each cycle by making the probability of contamination source(s) in each sub‐network as equal as possible, where sub‐networks are determined by the selected hydrants and current flow pipe directions. The CS's size is reduced at each cycle by exploiting sample testing results obtained in the previous cycle until there are no further hydrants to sample from. Two real‐world WDSs are used to demonstrate the effectiveness of the proposed MGSM. The results obtained show that the MGSM can significantly reduce the spatial range of the CS (to about 5% of the entire WDS) for a range of scenarios including multiple contamination sources and pipe flow direction changes. We found that an optimal number of sampling teams exists for a given WDS, representing a balanced trade‐off between detection efficiency and sampling/testing budgets. Due to its relative simplicity, the proposed MGSM can be used in engineering practice straightaway and it represents a viable alternative to the methods associated with water quality models and sensors. A new manual grab‐sampling method is proposed to localize continuous water distribution system (WDS) contamination sourcesThe new method employs a dynamic and cyclical sampling strategy based on WDS hydrantsProposed method is effective and efficient to localize contamination sources for many scenarios A new manual grab‐sampling method is proposed to localize continuous water distribution system (WDS) contamination sources The new method employs a dynamic and cyclical sampling strategy based on WDS hydrants Proposed method is effective and efficient to localize contamination sources for many scenarios

Urban drainage models (UDMs) are often used to manage urban flooding. However, these models generally involve many parameters to represent the underlying complex hydrodynamic processes. This results in significant challenges to achieving effective and robust model calibration especially with frequently limited observations, leading to unreliable model predictions. This paper makes the first attempt at UDM calibration using the Bayesian‐based Ensemble Smoother (ES) method. Three ES variants are considered, that is, the primary ES, the versions with multiple data assimilation (ES‐MDA) and iterative local update (ES‐ILU). Two synthetic cases and one real‐world application with up to 5,236 calibration parameters are tested. Results obtained show that: (a) both ES‐MDA and ES‐ILU can produce effective model calibration with ES‐ILU outperforming ES‐MDA in terms of both accuracy and uncertainty while ES exhibits limited performance; (b) for the real‐world case, both the ES‐MDA and ES‐ILU methods provide better calibration results than the best‐known solution manually obtained, (c) a minimum number of observations are required to enable an overall accurate model calibration (e.g., four and ten more monitoring sites are needed in the two synthetic cases); and (d) the model calibrated using an intense rainfall event is generally robust to make reliable predictions across different rainfall events while the model calibrated using less intense rainfall event does not perform well for more intense rainfall events. It was also found that ubiquitous parameter equifinality significantly hinders unique parameter identification even when overall accurate state estimates are obtained. This should be clearly understood in practical applications. Urban floods have been a serious disaster worldwide. Urban drainage models (UDMs) have been widely used to facilitate flooding prevention and mitigation. However, a challenge associated with the UDMs is that a large number of parameters need to be specified and calibrated. While some optimization‐based or manual calibration methods are available, their efficiency or accuracy are often unsatisfactory. This can lead to unreliable predictions of the rainfall‐runoff process. To this end, this paper proposes the ensemble smoother (ES) methods to calibrate the UDMs. Benefits of these ES methods include the great efficiency, high accuracy and the identification of the uncertainty when calibrating model parameters. These conclusions are based on results of two synthetic cases and one real‐world UDM. Ensemble smoothers are promisingly effective and robust methods to calibrate urban drainage modelsUbiquitous parameter equifinality hinders unique parameter identification, which is a concern for practical applicationsEnsemble smoothers are validated on a real‐world case, providing insights into how improved model performance can be achieved Ensemble smoothers are promisingly effective and robust methods to calibrate urban drainage models Ubiquitous parameter equifinality hinders unique parameter identification, which is a concern for practical applications Ensemble smoothers are validated on a real‐world case, providing insights into how improved model performance can be achieved

In October 2011, Section 105A (S105A) of the Water Act of 2003 transferred the ownership of previously privately owned sewers to the ten water and sewerage companies operating in England and Wales. In light of this recent legislative change, this paper discusses the asset management challenges associated with the private sewer transfer before exploring a decision-making framework used by South West Water to establish a more accurate understanding of the extent and likely condition of their newly transferred network. The framework has allowed South West Water to initiate a proactive asset management programme with the aim of addressing the deteriorating condition of these assets while also tackling their associated serviceability performance. Initially, a number of geospatial (GIS) tools are used to provide an estimate of the likely extent of the transferred network before a well-established public sewer deterioration model is used to predict the condition and operational performance of these S105A assets over time. The outputs from this study have better equipped decision makers with information surrounding the deterioration and collapse rates of these newly transferred sewers over time. The information has been used to help formulate strategic business planning decisions, for example, by using cost vs. benefit analysis tools to model different investment scenarios across the network.

This paper describes the development of an adaptive locally constrained genetic algorithm (ALCO-GA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world nonlinear optimisation problems, including water system design and maintenance problems. In this work we propose a heuristic-based approach to the mutation of chromosomes with the algorithm employing an adaptive mutation operator which utilises hydraulic head information and an elementary heuristic to increase the efficiency of the algorithm's search into the feasible solution space. In almost all test instances ALCO-GA displays faster convergence and reaches the feasible solution space faster than the standard genetic algorithm. ALCO-GA also achieves high optimality when compared to solutions from the literature and often obtains better solutions than the standard genetic algorithm. [ABSTRACT FROM AUTHOR]

A hydraulic model-based emergency scheduling Decision Support System (DSS) is designed to eliminate the impact of sudden contamination incidents occurring upstream in raw water supply systems with multiple sources. The DSS consists of four functional modules, including water quality prediction, system safety assessment, emergency strategy inference and scheduling optimization. The work flow of the DSS is as follows. First, the water quality variations on specific cross-sections are calculated given the pollution information. Next, a comprehensive evaluation on the safety of the current system is conducted using the outputs in the first module. This will assist in the assessment of whether the system is in danger of failure, taking both the impact of pollution and system capacity into account. If there is a severe impact of contamination on the reliability of the system, a fuzzy logic based inference module is employed to generate reasonable strategies including technical measures. Otherwise, a Genetic Algorithm (GA)-based optimization model will be used to find the least-cost scheduling plan. The proposed DSS has been applied to a coastal city in South China during a saline tide period as validation. Through scenario analysis, it is demonstrated that this DSS tool is instrumental in emergency scheduling for the water company to quickly and effectively respond to sudden contamination incidents.

Agricultural nonpoint source pollution has been identified as one of the leading causes of surface water quality impairment in the United States. Such an impact is important, particularly in predominantly agricultural areas, where application of agricultural fertilizers often results in excessive nitrate levels in streams and rivers. When nitrate concentration in a public water supply reaches or exceeds drinking water standards, costly measures such as well closure or water treatment have to be considered. Thus, having accurate nitrate-N predictions is critical in making correct and timely management decisions. This study applied a set of data mining tools to predict weekly nitrate-N concentrations at a gauging station on the Sangamon River near Decatur, Illinois. The data mining tools used in this study included artificial neural networks, evolutionary polynomial regression and the naive Bayes model. The results were compared using seven forecast measures. In general, all models performed reasonably well, but not all achieved best scores in each of the measures, suggesting that a multi-tool approach is needed. In addition to improving forecast accuracy compared with previous studies, the tools described in this study demonstrated potential for application in error analysis, input selection and ranking of explanatory variables, thereby designing cost-effective monitoring networks. [ABSTRACT FROM AUTHOR]

The multi-objective design and rehabilitation of water distribution systems (WDS) is defined as the search for the set of system designs which offers the best trade-off between competing design objectives. Typically these objectives will consist of the cost of implementing a system design and a measure of the performance of that system. These measures are often in competition since improvements in the performance of a system generally come at a cost. Here three genetic algorithms which use probabilistic methods to identify building blocks—the Univariate Marginal Distribution Algorithm (UMDA) (Mühlenbein 1997), the hierarchical Bayesian Optimisation Algorithm (hBOA) (Pelikan 2002) and the Chi-Square Matrix methodology (Aporntewan & Chongstitvatana 2004)—are compared to the well-known multi-objective evolutionary algorithm NSGAII (Deb et al. 2002) for the multi-objective design and rehabilitation of water distribution systems. For single-objective problems the identification of building blocks has been seen to make evolutionary algorithms more scalable to large problems than simple genetic algorithms. In this paper these algorithms are shown to offer significantly better solutions than NSGA-II for the case of large systems. However, this improvement comes at the expense of diversity of solutions in the fronts identified. [ABSTRACT FROM AUTHOR]

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This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to employ parameter estimation to obtain constants using least squares. The new technique, termed Evolutionary Polynomial Regression (EPR) overcomes shortcomings in the GP process, such as computational performance; number of evolutionary parameters to tune and complexity of the symbolic models. Similarly, it alleviates issues arising from numerical regression, including difficulties in using physical insight and over-fitting problems. This paper demonstrates that EPR is good, both in interpolating data and in scientific knowledge discovery. As an illustration, EPR is used to identify polynomial formulæ with progressively increasing levels of noise, to interpolate the Colebrook-White formula for a pipe resistance coefficient and to discover a formula for a resistance coefficient from experimental data. [ABSTRACT FROM AUTHOR]

An expanded rehabilitation of the hypothetical water distribution network of Anytown, USA is considered. As well as pipe rehabilitation decisions, tank sizing, tank siting and pump operation schedules are considered as design variables. Inclusion of pump operation schedules requires consideration of water system operation over the demand pattern period. Design of distribution storage facilities involves solving numerous issues and trade-oils such as locations, levels and volume. This paper investigates the application of multi-objective evolutionary algorithms in the identification of the pay-off characteristic between total cost, reliability and water quality of Anytown's water distribution system. A new approach is presented for formulation of the model. To provide flexibility, the network must be designed and operated under multiple loading conditions. The cost of the solution includes the capital costs of pipes and tanks as well as the present value of the energy consumed during a specified period. Optimization tends to reduce costs by reducing the diameter of, or completely eliminating, pipes, thus leaving the system with insufficient capacity to respond to pipe breaks or demands that exceed design values without violating required performance levels. Here a resilience index is considered as a second objective to increase the hydraulic reliability and the availability of water during pipe failures. Considering reliability as one of the objectives in the optimization process will decrease the level of vulnerability for the solutions and therefore will result in robust networks. However, oversized distribution mains and storage tanks will have adverse effects on water age with negative effects on water quality due to low flow velocity and little turnover, respectively. Therefore, another objective in the design and operation of distribution systems with storage facilities is the minimization of residence time, thus minimizing deterioration in water quality, which is directly associated with the age of water. Residence time must include not only the time in tanks but also the travel time before and after the water's entry into the storage facilities. The residence time of the water in the network is considered as a surrogate measure of water quality. Results are presented for the pay-off characteristics between total cost, reliability and water quality, for 24 h design and five loading conditions. [ABSTRACT FROM AUTHOR]

The extended-period (time-varying or dynamic) equations describing incompressible flow in pipe networks can be classified mathematically as a set of first-order, non-homogenous, non-linear differential equations. Since this set of equations cannot normally be solved analytically, numerical integration or regression methods are typically used. In this paper, a new method for extended-period simulation, called the explicit integration method, is proposed for water pipe networks without demands. The method is based on the premise that a complex water pipe network can be represented by a number of simple base networks. The simple base networks are selected in such a way that their dynamic equations can be solved through explicit integration. In this paper a simple base network consisting of a fixed-head reservoir feeding a tank through a single pipeline is analyzed. It is then illustrated how a complex water pipe network can be decoupled into its constituent simple base networks and its dynamic behavior simulated using a step-wise procedure. The explicit integration method is then compared to the commonly used Euler numerical integration method. It is shown that the accuracy of the explicit integration method is considerably better than that of the Euler method for the same computational effort.

Leakage detection and calibration of hydraulic models are important issues for the management of water and other distribution networks. An inverse transient model based on a hybrid search technique is presented here. The inverse model is developed mainly for the detection of leaks in water distribution networks. The inverse transient procedure is formulated as a constrained optimisation problem of weighted least-squares type. Two optimisation techniques are tested: the genetic algorithm (GA) and the Levenberg-Marquardt (LM) method. After examining their performance, a new hybrid genetic algorithm (HGA) is developed to exploit the advantages of combining the two methods. The resulting HGA-based inverse transient model is compared with the GA and LM-based inverse transient models using two case studies. The HGA-based inverse transient model proved to be more stable than the LM-based model and it is more accurate and much faster than the GA-based inverse transient model.

The recently introduced Structured Messy Genetic Algorithm model for optimising water distribution network rehabilitation is expanded to include not only pipe rehabilitation decisions but also pumping installations and storage tanks as variables. The formulation of the model is detailed with two approaches presented for handling the design of storage within the system. The application of the model to the benchmark “Anytown” problem is used as an example of its capabilities, with cheaper designs being produced than any previously published.

Many modifications, extensions, discussions, and evaluations of chance-constrained reservoir operating models have been reported in the technical literature. Lack of economic data and the fact that the establishment of acceptable risk levels in these types of models involves a human factor with all its vagueness of perception, subjectivity, and attitudes may not permit proper application of either reliability or multiobjective programming approaches. This paper presents a unique methodology for handling a practical problem of selecting risk levels in chance-constrained reservoir operation modeling. The proposed methodology is based on fuzzy set theory. Two types of fuzzy sets are used in the formulation of the reservoir long-term planning model, one for constraints and one for the objective function. An iterative solution algorithm for deriving an optimal decision using fuzzy set operations and the chance-constrained approach is developed and presented. A practical application of the approach demonstrates the feasibility and efficiency of both the proposed approach and its iterative search procedure for selecting risk levels in chance-constrained reservoir modeling.

Leakage losses in a water distribution network increase significantly for higher pressures and an obvious way of reducing losses is by reducing network pressures. This paper presents a methodology for pressure regulation in a water distribution network using an evolution program, encompassing the principles of evolutionary design and genetic algorithms. The optimization problem of minimizing the pressure heads is formulated with the settings of isolating valves as decision variables and minimum allowable pressures as constraints. The algorithm developed incorporates a steady-state network hydraulic analysis model based on the linear theory method. Computational results for two example networks demonstrating the effectiveness of the methodology are presented.

Reservoir management and operations are very complex activities in the field of water resources engineering. During the last two decades, a number of systems analysis techniques, particularly optimization and simulation, have been adopted for reservoir planning, design and operations. Recently, the area of knowledge‐based engineering (expert systems) has emerged as a potential technique for incorporating human expertise and some degree of intelligent judgement into decision‐supporting software. This paper presents the potential benefits of this technology in the area of reservoir management and operations. The presentation is illustrated with an example of an engineering expert system for reservoir analysis that is currently in development.

This paper proposes a multistage method for burst leak localization through valve operations (VOs) and smart demand metering in district meter areas (DMAs) of water distribution systems (WDSs). Each stage includes partitioning of the DMA into two subregions using VOs and identification of potentially leaking pipes within the subregions through water balance analysis based on smart demand meters. Such a process is performed repeatedly (multiple stages) to narrow down the spatial range for pinpointing leak locations. To improve efficiency, a bisection optimization problem is formulated to localize the minimum leak areas using the lowest number of VOs, which is solved by a graph theory‐based method. The utility of the proposed method is demonstrated using two DMAs (DMA1 and DMA2) of a real WDS with different topological properties. Results show that the proposed method can efficiently localize artificial burst leaks in DMA1 within 7–15% of the total pipe length, implying that the proposed method is theoretically effective in localizing pipe burst leaks. The real application to DMA2 has identified two leak regions with 2.3 and 4.2 km of pipe length (around 3–6% of the entire DMA2) using 18 VOs. These two burst leaks have been subsequently confirmed and pinpointed using listening rods by practitioners of the local water utility. These results indicate that the proposed multistage method is effective and efficient for burst leak localization, which can be promising for wide practical applications due to rapid developments of smart WDSs (e.g., smart demand meters or control valves). A multistage method is developed to localize pipe burst leaks using valve operationsThe graph theory is employed to efficiently identify optimal valve operationsThe proposed method is demonstrated by practical application to a real DMA

Capacity of urban drainage systems (UDSs) can substantially influence flooding properties of urban catchments. This motivates many studies to optimally design UDSs often using multiobjective evolutionary algorithms (MOEAs) as they can explore trade‐offs between conflicting objectives (e.g., cost vs. system reliability). However, MOEA‐based approaches are typically computationally demanding and their solutions are often practically unacceptable as engineering domain knowledge is often not explicitly considered. To address these two issues, this paper proposes an efficient optimization framework for UDS design, where an engineering‐based design method (EBDM) is developed to generate approximate solutions to initialize the MOEA's search, thereby greatly enhancing the optimization efficiency. To improve the solution practicality, two ideas have been implemented in the proposed optimization method (PM): (i) the variability of peak depths across pipes is minimized and (ii) a constraint is introduced to ensure that sizes of pipes in the downstream direction are no smaller than their corresponding upstream diameters. Two real‐world UDSs of different size are used to demonstrate the effectiveness of the PM. Results show that (i) the proposed EBDM is effective in producing initial solutions that are very close to the final solutions identified by the optimization methods, (ii) the minimization of the variability of peak depths in pipes is practically meaningful as it can facilitate to identify solutions with great ability in handling future uncertainties (e.g., rainfall variability), and (iii) the PM significantly improves optimization efficiency and solution practicality compared to the traditional optimization approach, with benefits being more prominent for larger UDSs. An efficient engineering‐based method is proposed to generate initial solutions that account for both pipe diameters and slopesThe minimization of variability of peak depths across pipes is effective to handle future uncertaintiesThe proposed method is significantly more efficient and practically meaningful than traditional approaches for UDS design problems