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Recently, various detection approaches that identify anomalous events (e.g., discoloration, contamination) by analyzing data collected from smart meters (so-called structured data) have been developed for many water distribution systems (WDSs). However, although some of them have showed promising results, meters often fail to collect/transmit the data (i.e., missing data) thus meaning that these methods may frequently not work for anomaly identification. Thus, the clear next step is to combine structured data with another type of data, unstructured data, that has no structural format (e.g., textual content, images, and colors) and can often be expressed through various social media platforms. However, no previous work has been carried out in this regard. This study proposes a framework that combines structured and unstructured data to identify WDS water quality events by collecting turbidity data (structured data) and text data uploaded to social networking services (SNSs) (unstructured data). In the proposed framework, water quality events are identified by applying data-driven detection tools for the structured data and cosine similarity for the unstructured data. The results indicate that structured data-driven tools successfully detect accidents with large magnitudes but fail to detect small failures. When the proposed framework is used, those undetected accidents are successfully identified. Thus, combining structured and unstructured data is necessary to maximize WDS water quality event detection.

It is necessary to predict dam inflow in advance for flood prevention and stable dam operations. Although predictive models using deep learning are increasingly studied, these existing studies have merely applied the models or adapted the model structure. In this study, data preprocessing and machine learning algorithms were improved to increase the accuracy of the predictive model. Data preprocessing was divided into two types: The learning method, which distinguishes between peak and off seasons, and the data normalization method. To search for a global solution, the model algorithm was improved by adding a random search algorithm to the gradient descent of the Multi-Layer Perceptron (MLP) method. This revised model was applied to the Soyang Dam Basin in South Korea, and deep learning-based discharge prediction was performed using historical data from 2004 to 2021. Data preprocessing improved the accuracy by up to 61.5%, and the revised model improved the accuracy by up to 40.3%. With the improved algorithm, the accuracy of dam inflow predictions increased to 89.4%. Based on these results, stable dam operation is possible through more accurate inflow predictions.

In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies.

A traditional urban drainage system (UDS) planning approach generally considers the most probable future rainfall scenario. However, this single scenario (i.e., scenario-optimal) planning approach is prone to failure under recent climatic conditions, which involve increasing levels of uncertainty. To overcome this limitation, an alternative is to consider multiple scenarios simultaneously. A two-phase multi-scenario-based UDS planning approach was developed. Scenario-optimal solutions were determined for a set of scenarios in Phase I, as the traditional planning approach, while common elements across the scenarios were identified and used to consider components-wise regret cost concept for Phase II optimization, from which a compromise solution was sought. The storm water management model was dynamically linked with the harmony search algorithm for each phase optimization model. The proposed approach was demonstrated in the planning of the grid-type drainage networks of S-city. The compromise solution was compared with the scenario-optimal solutions (Phase I) with respect to cost effectiveness and system performance under scenarios that were not considered in the planning phase.

This editorial summarizes the 11 papers published in the Special Issue entitled “Resilient and Robust Water Distribution Systems: State-of-the-Art and Research Challenges” which were classified into five themes related to water distribution systems (WDSs): (1) state-of-the-art review on WDS resilience and robustness (ROB), (2) WDS performance quantification and recovery under earthquakes, (3) criticality analysis and visualization, (4) novel design methodologies, and (5) hydraulic parameter monitoring for WDS rapidity improvement. Following the provision of the number of views and citations of each paper in a brief manner, a paper in category (1) that reviewed recent studies on WDS robustness is summarized. Category (2) covers three papers on improving the WDS capacity to fulfil customers’ demands in the case of an earthquake, a representative catastrophic failure event, while category (3) includes papers on visualization methods to represent the system’s criticality. The studies included in themes (4) and (5) proposed novel design methods and monitoring approaches for improving WDS resilience, respectively. Contributions from each study are described in the context of WDS resilience. We hope that this Special Issue can (1) serve as a reference point from which readers review progress, recent trends, and emerging issues, and (2) shed light on the appropriate future directions of WDS resilience studies.

Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously published studies. The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. k-fold cross-validation was used to assess the prediction reliability of the developed RELM model. The prediction results of the RELM model were evaluated using various error measures and compared with that of the standard extreme learning machine (ELM) and other methods presented in the literature. The findings of this research indicate that the compressive strength of HPC can be accurately estimated using the proposed RELM model.

This paper describes a study conducted to develop a method to facilitate more reliable determination of the rehabilitation priority order for water pipes by taking into account the pipes’ hydraulic importance. Existing methods use only the pipeline deterioration rate to determine the rehabilitation priority order. Accordingly, the deterioration rate under normal conditions and the hydraulic importance under abnormal conditions of water distribution pipelines were classified according to two different attributes. The deterioration rate of a water distribution pipeline was calculated in terms of the deterioration rate due to pipeline information factors and the deterioration rate resulting from the installation environment/external factors. The hydraulic importance of water distribution pipelines was calculated by considering the importance of a single pipe failure caused by water leakage or an accident and that of a multiple pipe failure caused by a disaster, such as an earthquake. These four attribute factors were employed in a multi-criteria decision-making process called a weighted utopian approach, developed in this study, that determines the final rehabilitation priority order for each pipeline. The study results indicate that the rehabilitation priority order can be determined more easily using this approach than with previously-developed methods and that the model developed is easier and more convenient to apply than existing rehabilitation priority order models that require a large amount of data, as well as complex failure probabilities and mathematical models.

Engineering benchmark problems with specific characteristics have been used to compare the performance and reliability of metaheuristic algorithms, and water distribution system design benchmarks are also widely used. However, only a few benchmark design problems have been considered in the research community. Due to the limited set of previous benchmarks, it is challenging to identify the algorithm with the best performance and the highest reliability among a group of algorithms. Therefore, in this study, a new water distribution system design benchmark problem generation method is proposed considering problem size and complexity modifications of a reference benchmark. The water distribution system design benchmark problems are used for performance and reliability comparison among several reported metaheuristic optimization algorithms. The optimal design results are able to quantify the performance and reliability of the compared algorithms which shows each metaheuristic algorithm has its own strengths and weaknesses. Finally, using the proposed method in this study, guidelines are derived for selecting an appropriate metaheuristic algorithm for water distribution system design.

A framework is presented to effectively improve the water distribution system (WDS) reliability. The proposed framework first classifies pipes in a WDS into three reinforcement types through topological analyses and hydraulic simulations over the WDS; type 1: no reinforcement, type 2: increasing pipe durability, and type 3: installing valve(s) at both ends. Then two rules, rules 1 and 2, are implemented, in which rule 1 first reinforces the pipe with the lowest reliability but rule 2 preferentially reduces damage size by pipe failures represented as the expected number of customers out of service (EN). The proposed method was applied to Cherry Hill network, and considerably improved the WDS reliability from 0.137 to 0.483. Both approaches showed notable differences in changes of the EN at each step of reinforcement, where rule 2 showed a better capability to reduce the EN and detect vulnerable areas in the WDS than rule 1. In addition, a practical approach, maximizing valve installations and minimizing pipe replacements according to the reinforcement types in the system, provided an improved WDS reliability (0.423) close to that of the rule-based approach (0.483) using only 21% of the construction cost by the rule-based approach. The proposed framework can be guidelines for improving the WDS reliability under restricted budget and site conditions.

This study compares the performance of self-adaptive optimization approaches in efficient water distribution systems (WDS) design and presents a guide for the selection of the appropriate method employing optimization utilizing the characteristic of each technique formulation. To this end, this study performs three types of analyses. First, the sensitivity analysis of each self-adaptive approach is conducted on single/multi-objective mathematical benchmark problems with various problem types (e.g., using solution shape or many local optimal solutions). Second, based on the applications and results of the mathematical problem, the performance of the algorithm is verified in the WDS design problem considering the minimum cost and the maximum system resilience under the single/multi-objective optimization framework. Third, the characteristics of search operators in the self-adaptive approach are compared according to the presence or absence of additional parameters and operators. Moreover, various performance indices are employed to compare the quantitative evaluation of each algorithm. Each algorithm is found to exhibit different characteristics depending on the problem scale and solution type. These results are expected to benefit future research in the formulation of new approaches and developments. Hence, this study provides rigorous testing of the performance of newly proposed algorithms in a highly simplified manner.

The resilience of a water distribution system (WDS) is defined as its ability to prepare, respond to, and recover from a catastrophic failure event such as an earthquake or intentional contamination. Robustness (ROB), one of the components of resilience, is the ability to maintain functionality to meet customer demands. Recently, the traditional probability-based system performance perspective has begun to shift toward the ROB and system performance variation point of view. This paper provides a state-of-the-art review of WDS ROB-based approaches proposed in three research categories: Design, operation, and management. While few pioneering works have been published in the latter two areas, an ROB indicator was proposed and thoroughly investigated for WDS design. Then, some future works are recommended in each of the three domains to promote developments in WDS ROB. Finally, a brief summary of this paper is presented, from which the final conclusions of the state-of-the-art review and recommendations are drawn. The new paradigm of WDS ROB-based design, operation, and management is in its infant stage and should be carved out in future studies.

Water supply facilities are vulnerable to extreme weather events, such as droughts and floods. To establish a sustainable solution that resists accidents and disasters, a distributed system is required. To supply high-quality tap water using the existing water-supply network, rechlorination facilities must be installed to secure residual chlorine at the pipe end. In this study, a process is developed to determine the injection points and dosages of rechlorination using the latest pressure-driven analysis. The method was compared to the results of demand driven analysis methods. The proposed model was applied to P City in Korea to draw results. A detailed evaluation was performed to study how water pressure head and demand-based hydraulic and water quality analysis results impact the injection points and dosages of rechlorination. Thus, the existing demand-based model shows significant spatial deviations in the pressure head in the presence of water pressure drops, which subsequently lead to over-estimation of chlorine injection dosages for maintaining the concentration of residual chlorine. However, the proposed model involves a numerically validated theory and draws more reasonable results for hydraulic, water quality, and rechlorination dosages. The proposed model can be used as a decision-making tool based on hydraulic analysis for the supply of water of a stable quality.

This study proposes a multi-objective optimal design approach for water distribution systems, considering mechanical system redundancy under multiple pipe failure. Mechanical redundancy is applied to the system’s hydraulic ability, based on the pressure deficit between the pressure requirements under abnormal conditions. The developed design approach shows the relationships between multiple pipe failure states and system redundancy, for different numbers of pipe-failure conditions (e.g., first, second, third, …, tenth). Furthermore, to consider extreme demand modeling, the threshold of the demand quantity is investigated simultaneously with multiple pipe failure modeling. The design performance is evaluated using the mechanical redundancy deficit under extreme demand conditions. To verify the proposed design approach, an expanded version of the well-known benchmark network is used, configured as an ideal grid-shape, and the multi-objective harmony search algorithm is used as the optimal design approach, considering construction cost and system mechanical redundancy. This optimal design technique could be used to propose a standard for pipe failure, based on factors such as the number of broken pipes, during failure condition analysis for redundancy-based designs of water distribution systems.

Recently, the number of extreme rainfall events has increased because of climate change. The ever-widening impervious area in urban watersheds also continuously augments runoff volume. Most measures to prevent urban inundation are structural, such as the construction, rehabilitation, and replacement of urban drainage facilities. Because structural measures require time and money, nonstructural measures are also required for the efficient prevention of urban inundation. Current operations in Korea focus on the individual operation of urban drainage facilities while neglecting the status of effluent streams. A study on urban drainage facilities that considers the status of urban streams is necessary to improve the operation of drainage facilities in urban areas. A revised resilience index is suggested to evaluate measures. For the historical rainfall event in 2010, the system resilience for current and integrated operations was 0.199 and 0.238, respectively. For the 2011 event, the system resilience for current and integrated operations was 0.064 and 0.235, respectively. The integrated operation exhibited good performance for the 2010 and 2011 events. Based on the results of this study, an operation as a nonstructural measure for the total management of urban areas is proposed. The revised resilience index could support decision-making processes for flood-management plans.

Traditional inter-event time definition (IETD) estimate methodologies generally take into account only rainfall characteristics and not drainage basin characteristics. Therefore, they may not succeed in providing an appropriate value of IETD for any sort of application to the design of urban drainage system devices. To overcome this limitation, this study presents a method of IETD determination that considers basin characteristics. The suggested definition of IETD is the time period from the end of a rainfall event to the end of a direct runoff. The suggested method can identify the independent events that are suitable for the statistical analysis of the recorded rainfall. Using the suggested IETD, the IETD of the Joong-Rang drainage system was determined and the area-IETD relation curve was drawn. The resulting regression curve can be used to determinate the IETD of ungauged urban drainage systems, with areas ranging between 40 and 4400 ha. Using the regression curve, the IETDs and time distribution of the design rainfall for four drainage systems in Korea were determined and rainfall-runoff simulations were performed with the Storm Water Management Model (SWMM). The results were compared with those from Huff's method which assumed a six-hour IETD. The peak flow rates obtained by the suggested method were 11%~15% greater than those obtained by Huff’s method. The suggested IETD determination method can identify independent events that are suitable for the statistical analysis of the recorded rainfall aimed at the design of urban drainage system devices.

Monthly flow predictions provide an essential basis for efficient decision-making regarding water resource allocation. In this paper, the performance of different popular data-driven models for monthly flow prediction is assessed to detect the appropriate model. The considered methods include feedforward neural networks (FFNNs), time delay neural networks (TDNNs), radial basis neural networks (RBFNNs), recurrent neural network (RNN), a grasshopper optimization algorithm (GOA)-based support vector machine (SVM) and K-nearest neighbors (KNN) model. For this purpose, the performance of each model is evaluated in terms of several residual metrics using a monthly flow time series for two real case studies with different flow regimes. The results show that the KNN outperforms the different neural network configurations for the first case study, whereas RBFNN model has better performance for the second case study in terms of the correlation coefficient. According to the accuracy of the results, in the first case study with more input features, the KNN model is recommended for short-term predictions and for the second case with a smaller number of input features, but more training observations, the RBFNN model is suitable.

This study proposes a framework for improving the robustness of water distribution systems using the optimal valve installation approach with regard to system reinforcement. To improve the robustness in valve design, this study introduces critical segment selection technique and optimal valve location determination within the concept of segment. Using the segment finding algorithm, the segment and the unintentional isolation can be identified. To select the critical segment, a multicriteria decision technique is applied by considering the hydraulic, social and economic effect. Finally, the optimal valve locations and the number of additional valves is determined by pipe failure analysis through the trade-off relationship with the number of additional valves and the maximum damage under pipe failure situations. To verify the proposed technique, the real-world water distribution systems are applied and compared with the original design.

The Muskingum flood routing model is a representative flood routing model. The field applicability of the Muskingum flood routing model is known to be good, and the structure of input data is simple. However, accurate flood routing cannot be conducted using current Muskingum flooding routing models due to the structural limitation of equations. The advanced nonlinear Muskingum flood routing model is suggested for improving accuracy, considering continuous flow using weighted inflow. Continuous flow means the past continuous inflows, including first and secondary inflow over time. Five flood data were selected for a comparison between the results of this study and previous ones. The sum of squares, root mean square errors, and Nash-Sutcliffe efficiency are applied in order to calculate the error values. The vision correction algorithm was used to estimate parameters in the new model. Generally, the new method yields better results than those described in previous studies, though it shows similar results with the most recent methods (NLMM-L) in some flood data. Finally, the new method and NLMM-L are applied for the prediction of Daechung flood data in Korea. The new method is useful in the prediction of outflows, because it shows better results than NLMM-L.

In order to achieve the required residual chlorine concentration at the end of a water network, the installation of a re-chlorination facility for a high-quality water supply system is necessary. In this study, the optimal re-chlorination facility locations and doses were determined for real water supply systems, which require maintenance in ord3r to ensure proper residual chlorine concentrations at the pipeline under the present and future conditions. The harmony search algorithm (HSA), which is a meta-heuristic optimization technique, was used for the optimization model. This method was applied to two water supply systems in South Korea and was verified through case studies using different numbers of re-chlorination points. The results show that the proposed model can be used as an efficient water quality analysis and decision making tool, which showed the optimal re-chlorination dose and little deviation in the spatial distribution. In addition, the HSA results are superior to those of the genetic algorithm (GA) in terms of the total injection mass with the same number of evaluations.

Imperviousness has increased due to urbanization, as has the frequency of extreme rainfall events by climate change. Various countermeasures, such as structural and nonstructural measures, are required to prepare for these effects. Flood forecasting is a representative nonstructural measure. Flood forecasting techniques have been developed for the prevention of repetitive flood damage in urban areas. It is difficult to apply some flood forecasting techniques using training processes because training needs to be applied at every usage. The other flood forecasting techniques that use rainfall data predicted by radar are not appropriate for small areas, such as single drainage basins. In this study, a new flood forecasting technique is suggested to reduce flood damage in urban areas. The flood nomograph consists of the first flooding nodes in rainfall runoff simulations with synthetic rainfall data at each duration. When selecting the first flooding node, the initial amount of synthetic rainfall is 1 mm, which increases in 1 mm increments until flooding occurs. The advantage of this flood forecasting technique is its simple application using real-time rainfall data. This technique can be used to prepare a preemptive response in the process of urban flood management.

A meter network is a set of meters installed throughout a water distribution system to measure system variables, such as the pipe flow rate and pressure. In the current hyper-connected world, meter networks are being exposed to meter failure conditions, such as malfunction of the meter’s physical system and communication system failure. Therefore, a meter network’s robustness should be secured for reliable provision of informative meter data. This paper introduces a multi-objective optimal meter placement model that maximizes the detection probability, minimizes false alarms, and maximizes the robustness of a meter network given a predefined number of meters. A meter network’s robustness is defined as its ability to consistently provide quality data in the event of meter failure. Based on a single-meter failure simulation, a robustness indicator for the meter network is introduced and maximized as the third objective of the proposed model. The proposed model was applied to the Austin network to determine the independent placement of pipe flow and pressure meters with three or five available meters. The results showed that the proposed model is a useful tool for determining meter locations to secure high detectability and robustness.

Pump stations prevent backwater effects from urban streams and safely drain rainwater in urban areas. Urbanization has increased the required capacity of centralized reservoirs and drainage pumps; yet, their respective designs are based on the runoff of the target watershed at the time of design. In Korea, additional pump stations are constructed to supplement the insufficient capacity of centralized reservoirs and drainage pumps. Two pump stations in the same drainage area share centralized reservoirs, and there are gates between them. Operation of the gates and drainage pumps is based on the water level in the connected centralized reservoirs. The convertible operation is based on changes in flow between two pump stations with different effluent streams in shared centralized reservoirs. Efficient distribution of inflow to both pump stations provides additional storage capacity in centralized reservoirs and rapid drainage. For a rainfall event in 2010, flooding volumes for current and convertible operations were 58,750 and 7507 m3, respectively. For an event in 2011, the corresponding figures were 3697 and 471 m3. This shows that resilience increased by 0.10829 and 0.00756, respectively, for the two events. Accordingly, a new technique to operate multiple pump stations for reducing urban inundation is proposed.

We developed a hybrid algorithm for multi-objective design of water distribution networks (WDNs) in the present study. The proposed algorithm combines the global search schemes of differential evolution (DE) with the local search capabilities of harmony search (HS) to enhance the search proficiency of evolutionary algorithms. This method was compared with other multi-objective evolutionary algorithms (MOEAs) including NSGA2, SPEA2, MOEA/D and extended versions of DE and HS combined with non-dominance criteria using several metrics. We tested the compared algorithms on four benchmark WDN design problems with two objective functions, (i) the minimization of cost and (ii) the maximization of resiliency as reliability measure. The results showed that the proposed hybrid method provided better optimal solutions and outperformed the other algorithms. It also exhibited significant improvement over previous MOEAs. The hybrid algorithm generated new optimal solutions for a case study that dominated the best-known Pareto-optimal solutions in the literature

Poor drainage of urban storm water can lead to urban inundation which presents a risk to people and property. Previous research has presented various measures to prevent and reduce urban flooding and these measures can be classified into costly but effective structural measures, and economical but less effective non-structural measures. This study suggests a new approach to reduce urban flooding by combining structural and non-structural measures in a target watershed in Seoul, South Korea. Inlet design modification in a detention reservoir (Decentralized Reservoir, DR) is examined in conjunction with combined inlet/outlet management for the DR. Monitoring nodes used to control DR inlet/outlet operations are selected by locating the first flooding node, maximum flooding node and DR inlet node. This new approach demonstrates outstanding flood volume reduction for historical flooding events that occurred in Seoul during 2010 and 2011. Flood volumes during the 2010 event using the combined inlet/outlet operation in the DR were between 1656 m3 and 1815 m3 compared to a flood volume of 6617 m3 using current DR operation. Finally, the suggested operating level for the DR based on the best hydraulic section, system resilience index, and local regulations is 1.2 m.

This study proposes variable balancing approaches for the exploration (diversification) and exploitation (intensification) of the non-dominated sorting genetic algorithm-II (NSGA-II) with simulated binary crossover (SBX) and polynomial mutation (PM) in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives—minimizing the percent bias and minimizing three peak flow differences—are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization) in a benchmark hydrological calibration problem for the Leaf River (1950 km2) near Collins, Mississippi. Three performance metrics—solution quality, spacing, and convergence—are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods.

Failure of drainage systems leads to urban flooding; therefore, structural measures such as the installation of additional drainage facilities, including pump stations and detention reservoirs, have been adopted in the past to prevent and mitigate urban flooding. These measures, however, are costly and time consuming. To maximize flood mitigation efficiency, it is essential to also implement non-structural measures such as effective operation of drainage facilities. In this study, we propose a new cooperative operation scheme for urban drainage systems that involves linking centralized reservoir (CR) and decentralized reservoir (DR) operations by sharing water level information at monitoring nodes. Additionally, we develop a resilience index to assess the system's ability to mitigate, restore, and recover from inundation (i.e., failure). Most results show that flood reduction and resilience in cooperative operations are better than the current operation. However, the results of CR operation for 2010 are worse than the current operation at high monitoring node levels (1.4 m–1.5 m), and the results of DR operation for 2011 are worse than the current operation at low monitoring node levels (0.8 m–0.9 m). All results related to flood reduction and resilience in cooperative operation are superior to the current operation.

A detention reservoir is one of the most effective engineered solutions for flood damage mitigation in urban areas. Detention facilities are constructed to temporarily store storm water and then slowly drain when the peak period has passed. This delayed drainage may coincide with upstream floods and aggravate the flood risk downstream. Optimal operation and design are needed to improve the performance of detention reservoirs for flood reduction. This study couples hydrologic simulation software (EPA-SWMM) with an evolutional optimizer (extraordinary particle swarm optimization, EPSO) to minimize flood damage downstream while considering the inundation risk at the detention reservoir. The optimum design and operation are applied to an urban case study in Seoul, Korea, for historical severe flooding events and designed rainfall scenarios. The optimal facilities outperform the present facilities in terms of flood damage reduction both downstream and in the detention reservoir area. Specifically, the peak water level at the detention pond under optimal conditions is significantly smaller than that of the current conditions. The comparison of the total flooded volume in the whole watershed shows a dramatic reduction of 79% in a severe flooding event in 2010 and around 20% in 2011 and in 180 min designed rainfall scenarios.

Real-time state estimation is defined as the process of calculating the state variable of interest in real time not being directly measured. In a water distribution system (WDS), nodal demands are often considered as the state variable (i.e., unknown variable) and can be estimated using nodal pressures and pipe flow rates measured at sensors installed throughout the system. Nodes are often grouped for aggregation to decrease the number of unknowns (demands) in the WDS demand estimation problem. This study proposes an optimal node grouping model to maximize the real-time WDS demand estimation accuracy. This Kalman filter-based demand estimation method is linked with a genetic algorithm for node group optimization. The modified Austin network demand is estimated to demonstrate the proposed model. True demands and field measurements are synthetically generated using a hydraulic model of the study network. Accordingly, the optimal node groups identified by the proposed model reduce the total root-mean-square error of the estimated node group demand by 24% compared to that determined by engineering knowledge. Based on the results, more pipe flow sensors should be installed to measure small flows and to further enhance the demand estimation accuracy.

We proposed an economic, cost-constrained optimal design of a water distribution system (WDS) that maximizes seismic reliability while satisfying pressure constraints. The model quantifies the seismic reliability of a WDS through a series of procedures: stochastic earthquake generation, seismic intensity attenuation, determination of the pipe failure status (normal, leakage, and breakage), pipe failure modeling in hydraulic simulation, and negative pressure treatment. The network’s seismic reliability is defined as the ratio of the available quantity of water to the required water demand under stochastic earthquakes. The proposed model allows no pipe option in decisions, making it possible to identify seismic-reliability-based optimal layout for a WDS. The model takes into account the physical impact of earthquake events on the WDS, which ultimately affects the network’s boundary conditions (e.g., failure level of pipes). A well-known benchmark network, the Anytown network, is used to demonstrate the proposed model. The network’s optimal topology and pipe layouts are determined from a series of optimizations. The results show that installing large redundant pipes degrades the system’s seismic reliability because the pipes will cause a large rupture opening under failure. Our model is a useful tool to find the optimal pipe layout that maximizes system reliability under earthquakes.

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.