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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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Wastewater‐based epidemiology (WBE) has been proven to be a useful tool in monitoring public health‐related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater‐detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under‐represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically‐based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS‐CoV‐2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time‐resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back‐tracing of human‐excreted biomarkers based on only sampling at the outlet or other key points, but would require high‐frequency, contaminant‐specific sensor systems that are not available currently. Plain Language Summary: Viral outbreaks such as COVID‐19 are commonly monitored using traditional surveillance methods (e.g., nose swabs). However, individuals may not follow such testing schemes for different reasons, such as if they do not have symptoms. When people are infected by COVID‐19, they excrete SARS‐CoV‐2, the virus that causes the disease. Hence, analyzing sewage using wastewater‐based epidemiology (WBE) methods could help monitor the virus outbreak. Recent literature has identified a gap in combining machine learning and hydraulic models with WBE to detect viral outbreaks. In this study, we combine a hydraulic model that simulates virus transport with a machine learning model that tracks hotspot areas within a sewer network. The methodology developed in this paper was applied to a hypothetical sewer network to detect COVID‐19 disease hotspots rapidly. Results showed that the machine learning model has the potential to recognize hotspots but requires high time‐resolution data and is dependent on the sewer system's characteristics, such as flow velocity and virus sampling procedure. The developed methodology opens new possibilities for WBE to potentially trace human‐excreted viral genetic material based on only sampling at particular points. However, it would require high‐frequency sampling sensor systems that are not available currently. Key Points: Using a numerical model of wastewater network and machine learning has the potential for the early detection of viral outbreaksThe ability to recognize disease hotspots depends on sampling frequency and methodThe use of sewer models can improve the usefulness of wastewater‐based epidemiology data [ABSTRACT FROM AUTHOR]

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. Key Points: 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 [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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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). Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An integrated participatory approach based on Bayesian belief network (BBN) and evolutionary multiobjective optimization is proposed as an efficient decision-making tool in complex management problems. The proposed methodology incorporates all the available evidence and conflicting objectives to evaluate implications of alternative actions in the decision-making process and suggests best decision pathways under uncertainty. A BBN provides a framework within which the contributions of stakeholders can be taken into account. It allows a range of different factors and their probabilistic relationship to be considered simultaneously. It takes into account uncertainty by assigning probability to those variables whose states are not certain. The integration of BBN with evolutionary multiobjective optimization allows the analysis of tradeoff between different objectives and incorporation and acknowledgement of a broader set of decision goals into the search and decision-making process. The proposed methodology can be used as a decision support tool to model decision-making processes for complex problems. It deals with uncertainties in decision making pertaining to human behavior and checks for consistency of the developed BBN structure and the parameters of the probabilistic relationship by uncovering discrepancies in the decision analysis process (e.g., bias in completeness or redundancy of the model based on a utility function). It generates a set of efficient management options (appropriate combinations of interventions) that balances conflicting objectives. The effectiveness of the proposed methodology is discussed through application to a real case study. It is shown that it successfully identifies any inconsistencies in the developed BBN models and generates large numbers of management options that achieve an optimal tradeoff between different objectives. Integr Environ Assess Manag 2012; 8: 456-461. © SETAC [ABSTRACT FROM AUTHOR]

The water distribution system (WDS) design problem is defined here as a multiobjective optimization problem under uncertainty. The two objectives are (1) minimize the total WDS design cost and (2) maximize WDS robustness. The WDS robustness is defined as the probability of simultaneously satisfying minimum pressure head constraints at all nodes in the network. Decision variables are the alternative design options for each pipe in the network. The sources of uncertainty are future water consumption and pipe roughness coefficients. Uncertain variables are modeled using probability density functions (PDFs) assigned in the problem formulation phase. The corresponding PDFs of the analyzed nodal heads are calculated using the Latin hypercube sampling technique. The optimal design problem is solved using the newly developed RNSGAII method based on the nondominated sorting genetic algorithm II (NSGAII). In RNSGAII a small number of samples are used for each fitness evaluation, leading to significant computational savings when compared to the full sampling approach. Chromosome fitness is defined here in the same way as in the NSGAII optimization methodology. The new methodology is tested on several cases, all based on the New York tunnels reinforcement problem. The results obtained demonstrate that the new methodology is capable of identifying robust Pareto optimal solutions despite significantly reduced computational effort. [ABSTRACT FROM AUTHOR]

A new software tool, GACOMP, for the optimal design of general and symmetric/balanced laminates (or sandwich panels) with specified mechanical properties has been developed. The approach taken relies on the analysis of a given laminate, of known materials and stacking sequence, and on an efficient genetic algorithm-based optimization procedure to come up with the best design with respect to single or multiple objectives and constraints. Required engineering constants or their ratios, weight, strength, anisotropy, isotropy, or twist, considered individually or in any combination, can be used as optimization objectives. Several improvements to the genetic algorithms were introduced to provide faster convergence and the ability to identify multiple optima in a single run. Two practical examples are presented to show the effective performance of GACOMP The first tackles the design of a composite laminate with specified in-plane and flexural engineering constants, while the second deals with the design of a laminate with specified stiffness ratios, minimum twisting under the load considered, and maximum strength. [ABSTRACT FROM AUTHOR]

Use of mathematical models in water resource management has been plagued in the past by the lack of communication, understanding, and involvement of managers in the model development. Interactive modeling methods give managers an appropriate role in model use, calibration, and verification. This paper extends the idea of interactive reservoir modeling using the intelligent decision support system approach. An advisory tool developed using this approach integrates formal reservoir models with reservoir expertise for making both numerical and logical inferences. This integration required special treatment, programming skills, and representations. This tool is intended to help a user to select and use the proper formal model(s) for single multipurpose reservoir analysis. The use of the tool may improve the accuracy and effectiveness of information available to managers, decision makers, and researchers. A practical example of the Gruza reservoir study demonstrates the benefits of implementing the approach. [ABSTRACT FROM AUTHOR]

BSTRACT: An analysis of four streamflow generation schemes for the use in the estimation of the required conservation storage for a single reservoir is presented. The comparison of the generating schemes should aid in the selection of an appropriate model type for the reservoir sizing problem. The streamflow generation models are compared using two criteria. The first comparison is between the statistics of the generated streamflow sequences and the corresponding statistics from the historical record. The second evaluation compares the median reservoir size determined by each model with the required storage based on the historical flow sequence. The results of a comparative analysis for monthly streamflow data for the Rzav River in Yugoslavia are presented and discussed. The results indicate that both evaluation criteria are required to discriminate between the various options. [ABSTRACT FROM AUTHOR]