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In recent years, Cyber-Physical Systems (CPSs) have been applied to Water Distribution Systems (WDSs) to facilitate efficient operation and maintenance. Since data are transmitted through the network in such systems, a cyberattack can disrupt the operation of WDSs, for example, by causing water supply reduction, water pollution, and economic losses. In the past few years, cyberattack detection algorithms and various cyberattack scenarios have been proposed. These studies considered either hydraulic factors, such as pipe velocity, nodal pressure, or tank level, or water quality factors. However, an algorithm which considers only one factor cannot prevent the various problems that may arise, such as water quality issues, and the hydraulic and quality factors have a correlation. Therefore, in this study, a framework was developed by considering both hydraulic and water quality factors. The proposed approach was applied to an artificial neural network model. Performance indicators were used to examine the detection performance according to the parameters of the artificial neural network. By comparing the detection performance when only hydraulic factors were considered and the performance when both hydraulic and water quality factors were considered, the effectiveness of the algorithm that consider both hydraulic and water quality factors was demonstrated. A cyberattack detection algorithm that considers both hydraulic and water quality criteria can be applicable in more realistic scenarios and contribute to the establishment of safe infrastructure for the entire process of designing and operating WDSs with CPSs.

This study investigates the role of rainfall forecast system characteristics in predictive real-time optimal operation (PRTOP) of urban drainage systems (UDSs). A simulation-optimization model is proposed integrating the stormwater management rainfall-runoff simulator, the harmony search optimization algorithm, and a rainfall forecasting module. This module generates different rainfall forecast scenarios depending on the forecast (time) horizon (FH) and the forecast type (perfect or imperfect). Five adaptive PRTOP models are compared to evaluate the relationships between the FH, forecast type, and the system’s relative regulating capacity (SRRC). The models are tested in a part of UDS of Tehran, capital of Iran. Results indicate that for the studied system, perfect knowledge of future rainfall is more beneficial for a specific range of the SRRC, equal to 80–90% of its upper bound. Besides, the performance of the PRTOP model improves with increasing the FH up to a certain point, and then having no further positive effect. Finally, the PRTOP model equipped with a hypothetical forecasting model where the forecast error is a nonlinear function of the forecast lead time still performs better than both a zero-FH reactive RTOP model and short forecast horizon PRTOP models.

Efficient reservoir operation and irrigation scheduling are important for the mitigation of water shortages in Iran. For more accuracy, the hydrological and meteorological uncertainties associated with reservoirs and farm levels should be considered. The major contribution of the current paper is to evaluate the uncertainties of evapotranspiration (ET) and inflow, and the issue of constant/variable agricultural demand (CAD/VAD) for optimal irrigation scheduling and reservoir operation. Some optimization approaches were employed and compared during a drought episode in the Zayandeh‐Rud agricultural system. Approaches include: (i) DP‐CAD: dynamic programming (DP), considering CAD and no inflow uncertainty; (ii) SSDP‐CAD: sampling stochastic DP (SSDP) with CAD and inflow uncertainty; (iii) LP‐NLP‐VAD: implementing linear (LP) and non‐linear programming (NLP) modelling for crop types, growing stages, and irrigation systems under deterministic conditions; (iv) SDP‐NLP‐VAD: similar to the third approach, but considers ET uncertainties using a stochastic DP (SDP) rather than an LP model, and uses stochastic crop yield functions in the NLP formulation. DP‐CAD and SDP‐NLP‐VAD were the simplest and most complicated modelling processes, respectively. SDP‐NLP‐VAD was the most time‐consuming to reach a steady state and a global optimal solution. The LP‐NLP‐VAD and SDP‐NLP‐VAD approaches, which account for variability in crop water requirements, conservatively consider water shortages and reservoir release. © 2019 John Wiley & Sons, Ltd.

For reliable pressure-driven analysis (PDA) results, it is necessary to reasonably determine an objective head-outflow relationship (HOR) and the required head for each node. So far, no methodology has been proposed to objectively determine two factors based on data of real block. In this study, the HOR was defined using the water supply method, residential environment, and water consumption data within real blocks. The standard minimum residual pressure criteria were reviewed to quickly and rationally determine the required head for each node. To validate the proposed methodology, the HOR and required head application conditions were configured for different scenarios; the available flow rate of nodes and the water supply capacity of the entire block were evaluated based on PDA results. Through the results for each scenario, the HOR definition method was able to provide a reasonable result reflecting the actual block’s conditions, unlike the conventional orifice-type HOR, and the standard minimum residual pressure criteria as the required head was evaluated to be more efficient than the conventional time-consuming method. The HOR uncertainty and the lack of rationality in the selection of the required heads affected the reliability of PDA results; these problems can be solved using the proposed methodology.

This study proposes a novel detection model for the detection of cyber-attacks using remote sensing data on water distribution systems (i.e., pipe flow sensor, nodal pressure sensor, tank water level sensor, and programmable logic controllers) by machine learning approaches. The most commonly used and well-known machine learning algorithms (i.e., k-nearest neighbor, support vector machine, artificial neural network, and extreme learning machine) were compared to determine the one with the best detection performance. After identifying the best algorithm, several improved versions of the algorithm are compared and analyzed according to their characteristics. Their quantitative performances and abilities to correctly classify the state of the urban water system under cyber-attack were measured using various performance indices. Among the algorithms tested, the extreme learning machine (ELM) was found to exhibit the best performance. Moreover, this study not only has identified excellent algorithm among the compared algorithms but also has considered an improved version of the outstanding algorithm. Furthermore, the comparison was performed using various representative performance indices to quantitatively measure the prediction accuracy and select the most appropriate model. Therefore, this study provides a new perspective on the characteristics of various versions of machine learning algorithms and their application to different problems, and this study may be referenced as a case study for future cyber-attack detection fields.

Achieving reliable pressure-driven analysis (PDA) results that account for anomalies within water-pipe networks requires a head-outflow relationship (HOR) model that can calculate supply flow rate according to the supply pressure at each node. Many studies have suggested HOR models, but a methodology to define HORs that considers the actual residential environment of users and differing water-supply methods for residential buildings has not yet been proposed. This study considered water-use data from buildings and actual differences in residential environments in a surveyed area (including building heights, topography, and water systems within buildings) and water-supply methods (indirect/direct water supply, existence of a pump within buildings) to develop a methodology and derive a representative HOR for the target area for PDA. Further, a representative HOR was determined for each block by applying the developed methodology for two blocks with similar residential environments but different water-use patterns. It confirmed that the HOR induced through this process could provide high water-supply performance despite a low supply head and needed to reflect the diversity of the water-supply method. The proposed HOR-definition methodology can be easily applied in water-pipe network design and operation processes and ensure objectivity and rationality of HOR selection to yield reliable PDA results.

To reduce flood risk in urban regions, it is important to optimize the performance of operational elements such as gates and pumps. This paper compares the performances of two approaches of multi-period and single-period simulation-optimization that are used to derive real-time control policies for operating urban drainage systems. The EPA storm water management model (SWMM), converting real-time rainfall data to surface runoff at network control points, i.e. pump stations, is linked to the particle swarm optimization (PSO) algorithm, evaluating the system operation performance measure (objective function) for different sets of control policies. A prototype network in a portion of the Seoul urban drainage system is used to investigate the efficiency of the proposed approaches. Results justify the high efficiency of multi-period optimization, leading to 32 and 29% average reductions in peak water level violations from a pre-defined permissible threshold at target points and the number of pump switches, respectively, in comparison with the online single-period optimization. The myopic policies derived by single-period optimization are not reliable, and in some cases, they even perform worse than ad-hoc policies applied by system operators based on their past experiences.

Flood forecasting is a pre-emptive non-structural measure used to mitigate inundation. Most current flood forecasting techniques incorporate complex processes, such as training and optimization, before the technique can be applied. Conventional flood forecasting techniques, based on flood volume, provide alerts even if there is no significant risk of flood damage. In this study, a new flood forecasting technique has been developed based on likely flood damage using the multi-dimensional flood damage analysis method. This new flood forecasting technique overcomes the drawbacks of current flood forecasting techniques because it can be easily applied using rainfall data. The studied drainage area was divided into subareas, and the damage functions were obtained for each subarea using the flood volumes and damage information. Using these damage functions, the rainfall intensity when the flood damage initially occurred was calculated for each duration and subarea. The damage graph produced for flood forecasting in each subarea identified the rainfall intensities and durations that resulted from the initial occurrence of flood damage. This new flood forecasting technique could be used to save lives, valuable assets, and manage drainage areas.

The paper introduces a decentralized approach to optimizing the flood reduction efficiency of multiple detention reservoirs in urban areas. The flood reduction effects of a detention reservoir system were regionally assessed, and a new model was then created using an optimization technique which involved coupling extraordinary particle swarm optimization with a flood simulation engine, Storm Water Management Model, EPA-SWMM, to identify optimal outlet designs for a detention reservoir system. When applied to the To Lich drainage network (Hanoi, Vietnam) the proposed method outperformed the present design, reducing flooding under the design flood event. The performance of the approach was verified by comparing it with the overall objective function of total flooded volume for different design floods. The better performance of the decentralized optimization approach described here, in terms of local and regional flood management, illustrated its effectiveness for urban flooding reduction.

Metaheuristic Optimization Algorithms (MOA) have been widely employed for optimal designs of Water Distribution System (WDS). Generally, the size, capacity, and location of WDS components (e.g., pipes, pumps, and tanks) are determined in the WDS design. Therefore, solutions with good fitness have engineeringly sound value of decision variables and share commonality. For example, a solution with transmission pipes bigger than distribution pipes is better with respect to fitness than that with the opposite case in the pipe sizing problem. However, few efforts have been made to consider good fitness rules in the optimization. In this study, we develop a hybrid harmony search (HyHS) algorithm that combines Harmony Search (HS) and a rule induction algorithm. The proposed HyHS algorithm’s performance is compared with an improved GA, improved HS, and hybrid GA through optimizations of well-known benchmark functions and two WDS designs. The four algorithms are first applied to the optimization of the De Jong functions. Then, the least-cost and robustness-based design problems of New York tunnel system are solved using the four algorithms. The application results confirm that the proposed HyHS outperforms the other algorithms in terms of computational speed and effectiveness.

Rainfall in rainy seasons continuously occurs and it is hard to separate individual events which is a significant source for the design of drainage facility to reduce Combined Sewer Overflows (CSOs). The current design of drainage facilities is based on the maximum design for rainfall events despite its extensive use in urban areas. The design of drainage facility requires the characteristics of rainfall such as average annual rainfall volume, duration and intensity. The individual rainfall events in a continuous rainfall event can be divided by Inter-event Time Definition (IETD). Current techniques for the determination of IETD are autocorrelation analysis, the coefficient of variation analysis, and average annual number of rainfall events analysis. The problem of the existing three methods for IETD is an ambiguous result for the decision. The new method for the determination of IETD using exponential function is suggested. The probability density function of the continuous rainfall event is compared with the confidence range of regression curve generated from the exponential functions at each inter-event time. The comparison of current and new method for the determination of IETD is conducted in the target watershed.

The work here focused on developing a framework for the overall planning of water distribution network (WDN) using life cycle energy analysis (LCEA) method. During the life cycle, aging-based maintenance scheduling was also carried out. Three different networks that have a distinguishing range of pipe diameter sizes and total pipe lengths were selected for study networks in order to evaluate the scale dependency to energy usage trend and design pathway. The model is built up with a Visual Basic program, and the hydraulic network solver, EPANET 2.0, is linked to support simulation-based environmental assessment. The proposed model determines optimal diameter for minimum annual average energy usage (AAEU) while considering maintenance activities. For optimization, revised harmony search (ReHS) was selected, and also pipe aging and breakage models are used for maintenance scheduling. AAEU is calculated by dividing overall energy usage with life cycle. Proposed model is applied to three different scale WDN and demonstrated for three case studies. In the first case study, the proposed model was verified by comparing results with prior research studies. Then, LCEA was conducted for each of the network. Finally, an optimal design of each network was conducted. The first case study results matched well to prior research studies, thereby demonstrating that the applicability of the proposed model was verified. Results of the second case study showed that the AAEU is proportional to the scale of network while life cycle is inversely proportional. Maintenance activity had advantage for extending the planning period. Finally, optimal design results in case study 3, both AAEU and life cycle are proportional to the scale of WDN. Comparing second and third case results, AAEU results were mostly influenced by the scale of network, but the length of life cycle was more dependent on the distribution of diameters. In summary, AAEU can be reduced using two methods either by increasing the number of small pipes or extending the life cycle. The elicited results from the three cases are converging towards the importance of the pipe diameter distribution, number of small diameter pipes, and the total length of network. In order to reduce AAEU, the model considered the characteristics of the network and determined whether there are increases in the small diameter pipes or distributed diameters evenly. Results show that the suggested model could constitute an alternative design method for minimizing energy usage.

A simulation-optimization framework is presented for reliability-based optimal sizing, operation, and water allocation in the Bashar-to-Zohreh inter-basin water transfer project. The problem was formulated as a mixed integer nonlinear program (MINLP), for which two solution approaches were tested, including gradient-based nonlinear programming and simulation-optimization (SO). The SO framework linked the water evaluation and planning system (WEAP) simulation module, benefiting from fast, single-period linear programming, to the multi-objective particle swarm optimization (MOPSO) for multiperiod optimization. The objective functions were minimizing the sizes of the project’s infrastructures and maximizing the reliability of supplying water to agricultural lands. The combination of nonlinear programming and the branch-and-bound algorithm was not able to solve the resulting MINLP. The results of the MOPSO-WEAP framework indicated that the project can supply water for land development in the Dehdasht and Choram agricultural plains, located in the less developed Kohgiluye and Boyer-Ahmad Province of Iran at an acceptable reliability level. For example, for one of the Pareto solutions found corresponding to maximum land development (30,000 ha), an average volume of 237 million cubic meter (MCM) is transferred annually at a 73.2% reliability level with average sizes of water transfer and storage elements. Further, design-operation and hydropower scenarios were also evaluated, and the Pareto solutions were obtained.

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-o...

The optimal design of water distribution systems (WDSs) should be economical, consider practical field applicability, and satisfy hydraulic constraints such as nodal pressure and flow velocity. However, the general optimal design of a WDSs approach using a metaheuristic algorithm was difficult to apply for achieving pipe size continuity at the confluence point. Although some studies developed the design approaches considering the pipe continuity, these approaches took many simulation times. For these reasons, this study improves the existing pipe continuity search method by reducing the computation time and enhancing the ability to handle pipe size continuity at complex joints that have more than three nodes. In addition to more practical WDSs designs, the approach considers various system design factors simultaneously in a multi-objective framework. To verify the proposed approach, the three well-known WDSs to apply WDS design problems are applied, and the results are compared with the previous design method, which used a pipe continuity research algorithm. This study can reduce the computation time by 87% and shows an ability to handle complex joints. Finally, the application of this practical design technique, which considers pipe continuity and multiple design factors, can reduce the gap between the theoretical design and the real world because it considers construction conditions and abnormal situations.

A traditional single-scenario design approach considers the most probable future scenario, which is very risky and may result in high supplementary cost or overpayment (i.e., regret cost). ...

Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.

This work provides a comprehensive review of the quantitative measures of sustainability proposed for water distribution systems (WDSs) and their sustainable development. After a comprehensive literature review, eighteen studies overall, either clearly proposing quantitative measures of sustainability (three studies) or highlighting sustainable development (fifteen studies), were selected for a closer review. All three measures showed either a lack of applicability or were missing important aspects of sustainability. Additionally, they have not been thoroughly validated by demonstrating the measures under acceptable scenarios/conditions. The reviewed sustainable development practices showed that energy usage and greenhouse gas emissions, life cycle costing, and reliability were widely used to evaluate environmental, economic, and social impacts, respectively. The two primary recommendations made based upon reviews were to: (1) consider balancing usage (cost) and gain (benefit), rather than impacts; (2) consider indirect (cascading/consequential) interactions. Overall, existing measures of sustainability and sustainable development practices in WDSs must be advanced to accommodate a focus on restorative systems, as well as to maximize benefits and enable multidisciplinary and broader analyses.

Because of climate change, severe rainfall events that lead to lowland flooding and inundation problems are occurring more frequently, especially in urban areas, which have high population densities. In order to prevent inundation of urban areas, authorities in the Korean Government have set up integrated flood management plans and established spatial targets for rainfall runoff. These disaster prevention plans include measures such as improvements to the sewer pipe capacity and the construction of downstream pump stations, rainwater storage sites, and infiltration facilities. The main purpose of pervious design plans is to reduce the flood volume in target areas, and this research presents a new method for developing rainwater storage design plans while considering potential flood damage costs in urban areas. The new planning method for storage facility design was applied to a single watershed in the Sintaein basin in the city of Jeongeup, Jeonbuk, South Korea. The results indicated that flood damage costs could be reduced when this new concept for determining the location of storage facilities is used.

Multi-Objective Evolutionary Algorithms (MOEAs) are flexible and powerful tools for solving a wide variety of non-linear and non-convex problems in water resources engineering contexts. In this work, two well-known MOEAs, the Strength Pareto Evolutionary Algorithm (SPEA2) and Non-dominated Sorting Genetic Algorithm (NSGA2), and two additional MOEAs that are extended versions of harmony search (HS) and differential evolution (DE), are linked to the Environmental Protection Agency’s Storm Water Management Model (SWMM-EPA), which is a hydraulic model used to determine the best pipe replacements in a set of sewer pipe networks to decrease urban flooding overflows. The performance of the algorithms is compared for several comparative metrics. The results show that the algorithms exhibit different behaviours in solving the hydraulic rehabilitation problem. In particular, the multi-objective version of the HS algorithm provides better optimal solutions and clearly outperforms the other algorithms for thi...

This study aims to develop an economical tool for pipe size (diameter) design, i.e. a model, by using optimization techniques for agricultural irrigation systems. The developed model is validated through applications to an actual target region (Saemangeum reclamation area, zone 6). A newly developed metaheuristic optimization algorithm, termed a proportional genetic harmony search algorithm (PGHSA), is proposed in this study. To evaluate the optimization results of pipe diameters for the target region, the cost and hydraulic results of the initial and optimal designs are investigated by means of a comparison. Spatial equalization of nodal pressure and the economic velocity of each pipe are used as indicators for evaluating the hydraulic stability of the irrigation system. The results indicate that the proposed optimal design model can save construction cost considerably, by 1–11%, in comparison to the original plans. Copyright © 2016 John Wiley & Sons, Ltd.

Over the past two decades, flood risks have presented a significant challenge for urban areas owing to the increased peak flows resulting from urbanization and climate change. In metropolitan areas, large-scale networks of sewer pipes and pump stations are the main facilities used to mitigate flood damage. During flooding periods, when drainage gates are closed, pumping operations play a major role in efficiently reducing flood damage. To obtain an optimal policy for these operations, a novel robust approach is presented here. In this approach, a long-term operating rule is designed by coupling a mathematical model and a new hybrid harmony search algorithm, while considering the stochastic nature of rainfall events. Application of the proposed method to a real urban drainage system showed a high efficiency in terms of flood mitigation and performance of pumps compared to the current operating rule for the pump station. Compared to the traditional approach, optimal operation decreased peak water levels by an average of 40%, without increasing the number of pump switches. Based on these results, optimizing pumping operations appear to be a practical and highly effective way to reduce flood water levels and urban inundation without making changes to the actual infrastructure of the system.

Water distribution network decomposition, which is an engineering approach, is adopted to increase the efficiency of obtaining the optimal cost design of a water distribution network using an optimization algorithm. This study applied the source tracing tool in EPANET, which is a hydraulic and water quality analysis model, to the decomposition of a network to improve the efficiency of the optimal design process. The proposed approach was tested by carrying out the optimal cost design of two water distribution networks, and the results were compared with other optimal cost designs derived from previously proposed optimization algorithms. The proposed decomposition approach using the source tracing technique enables the efficient decomposition of an actual large-scale network, and the results can be combined with the optimal cost design process using an optimization algorithm. This proves that the final design in this study is better than those obtained with other previously proposed optimization algorithms.

The goal of the present study is to construct a reliability evaluation model of a water supply system taking seismic hazards and present techniques to enhance hydraulic reliability of the design into consideration. To maximize seismic reliability with limited budgets, an optimal design model is developed using an optimization technique called harmony search (HS). The model is applied to actual water supply systems to determine pipe diameters that can maximize seismic reliability. The reliabilities between the optimal design and existing designs were compared and analyzed. The optimal design would both enhance reliability by approximately 8.9% and have a construction cost of approximately 1.3% less than current pipe construction cost. In addition, the reinforcement of the durability of individual pipes without considering the system produced ineffective results in terms of both cost and reliability. Therefore, to increase the supply ability of the entire system, optimized pipe diameter combinations should be derived. Systems in which normal status hydraulic stability and abnormal status available demand could be maximally secured if configured through the optimal design.

This paper explores the effect of the horizon of foresight on future inflows on the minimum reservoir capacity required for meeting a specified yield. The goal is achieved by formulating the problem as a series of finite-horizon mixed integer linear programs (MILPs), each with a limited, partial foresight on future inflows. The MINLPs are then solved sequentially over a long-run planning horizon for determining the minimum reservoir capacity required for meeting a specified reservoir yield at a certain level of reliability. The results indicate although it is generally true that the required reservoir capacity will increase if the foresight on future inflows becomes more limited, an opposite result is also possible to occur depending on the system's characteristics and hydrology. Moreover, knowing about future inflows does not help any more after a certain level of foresight horizon. These findings provide insight into the issue of uncertainty of future inflows and their forecasts in the design and operation of reservoir systems.

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.

Many surrogate measures for water distribution system (WDS) reliability have been introduced in the last three decades. This study investigated the differences between designs based on topo...

A multiobjective optimal meter placement (MOMP) model is proposed for water distribution system (WDS) pipe burst detection to (1) minimize the normalized total meter cost, (2) maximize the ...

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.

State estimation (SE) involves estimating state variables of interest that cannot be directly measured by using measurable variables. In water distribution system (WDS) SE, nodes are often ...

The hydraulic analysis of water distribution networks (WDNs) is divided into two approaches: namely, a demand-driven analysis (DDA) and a pressure-driven analysis (PDA). In the DDA, the basic assumption is that the nodal demand is fully supplied irrespective of the nodal pressure, which is mainly suitable for normal operating conditions. However, in abnormal conditions, such as pipe failures or unexpected increase in demand, the DDA approach may cause unrealistic results, such as negative pressure. To address the shortcomings of DDA, PDA has been considered in a number of studies. For PDA, however, the head-outflow relation (HOR) should be given, which is known to contain a high degree of uncertainty. Here, the DDA-based simulator, EPANET2 was modified to develop a PDA model simulating pressure deficient conditions and a Monte Carlo simulation (MCS) was performed to consider the quantitative uncertainty in HOR. The developed PDA model was applied to two networks (a well-known benchmark system and a real-life WDN) and the results showed that the proposed model is superior to other reported models when dealing with negative pressure under abnormal conditions. In addition, the MCS-based sensitivity analysis presents the ranges of pressure and available discharge, quantifying service reliability of water networks.

We introduce a new system robustness index for optimizing the pump design and operation of water distribution systems. Here, robustness is defined as a system's ability to continue functioning under varying demand conditions. The maximum difference between the daily maximum and minimum pressures of a node was taken as a robustness indicator and incorporated as a constraint in a pump design and operation model that minimizes the total pump cost (construction and operation cost). Two well-known benchmark networks, the Apulian and Net2 networks, were modified and used to demonstrate the proposed model. The Pareto relationship between the total cost and system robustness was explored through independent optimizations of the model for different robustness constraint values. The resulting solutions were compared to the traditional least-cost solution. Regardless of the study networks, considering the robustness resulted in a greater number of small pumps compared with the least-cost solution. A sensitivity analysis on tank capacity was performed with the Apulian network. The proposed model is the pump design and operation tool that accounts for both the total pump cost and system robustness, which are the most important factors considered by water distribution operators.

In recent decades, evolutionary optimisation algorithms have been used successfully for a wide variety of water resources engineering problems and their applications are still increasing. In this research work, a hybrid harmony search algorithm, ‘Non-dominated Sorting Harmony Search’ algorithm is developed and compared with two state-of-the-art multi-objective evolutionary algorithms – the non-dominated sorting genetic algorithm (NSGA)-II and multi-objective particle swarm optimisation (MOPSO) algorithms – for assigning optimal rehabilitation plans for sewer pipe networks. The algorithms considered were validated using some standard test functions reported in the literature and compared with each other in terms of several metrics. These algorithms were then linked to the SWMM-EPA hydraulic model and applied to a storm sewer pipe network case study in Seoul, South Korea, to obtain the best rehabilitation plans for pipe replacements. The results showed that the algorithms considered have different behaviours in solving the benchmark tests and rehabilitation problem. The proposed hybrid multi-objective harmony search algorithm provides better optimal solutions in terms of different metrics and clearly outperforms the other two algorithms for the rehabilitation of the storm sewer pipe networks.

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

Recently, advanced and smart techniques are being implemented for improving water distribution system (WDS) management and control. Those methods are mostly based on field data measured in real-time throughout the system of bigdata characteristics especially with respect to its volume and velocity. An interesting research issue is to investigate how to extract useful information from big data for efficient WDS management and control (e.g., pipe burst and leakage detection). This study applies the Western Electric Company (WEC) method, a statistical process control method, for pipe burst detection which plots field data measured in real-time around control limits obtained from historical normal field measurements. We investigate the impact of meter location and the number of meters on pipe burst detectability (i.e., detection probability and false alarm rate). Control and out-of-control pipe flow data are synthetically generated by using a hydraulic model of the Austin network and simulating pipe bursts under stochastic demand conditions.

The individual and combined effects of structural and nonstructural measures on urban drainage system resilience were investigated in this study. The resilience of an urban drainage system ...

Flooding volume in urban areas is not linearly proportional to flooding damage because, in some areas, no flooding damage occurs until the flooding depth reaches a certain point, whereas flooding damage occurs in other areas whenever flooding occurs. Flooding damage is different from flooding volume because each subarea has different components. A resilience index for urban drainage systems was developed based on flooding damage. In this study, the resilience index based on flooding damage in urban areas was applied to the Sintaein basin in Jeongup, Korea. The target watershed was divided into five subareas according to the status of land use in each subarea. The damage functions between flooding volume and flooding damage were calculated by multi-dimensional flood damage analysis. The extent of flooding damage per minute was determined from the results of flooding volume per minute using damage functions. The values of the resilience index based on flooding damages were distributed from 0.797292 to 0.933741. The resilience index based on flooding damage suggested in this study can reflect changes in urban areas and can be used for the evaluation of flood control plans such as the installation, replacement, and rehabilitation of drainage facilities.

Water distribution network (WDN) optimization design have been applied various metaheuristic optimization algorithms (i.e., genetic algorithm, particle swarm optimization, harmony search algorithm, and etc.) and have been efforts to improve the final solution quality. In contrast, in multi-objective problem framework, these kinds of efforts are lacking compared to other fields such as mathematics and other civil infrastructure designs. Therefore, in this study, we developed five approaches to improving the quality of the final solution to a WDN design problem: three warm initial solution approaches, collectively referred to as single-multi-optimization approaches (SMO-1, SMO-2, and SMO-3); a post-optimization approach, referred to as multi-single-optimization (MSO); and (3) a guided search approach based on engineering knowledge, referred to as guided search optimization (GSO). The approaches were embedded within the multi-objective harmony search (MOHS) framework and used to find Pareto optimal designs for well-known benchmark networks considering two objectives such as network construction cost and system resilience. The final results were compared using two kinds of performance indices represented the solution diversity and convergence. The application results show that the proposed warm initial solution approaches make better final Pareto solutions and improve performance of optimization compared to other metaheuristic optimization algorithms in terms of computational efficiency.

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 m(3) and 1815 m(3) compared to a flood volume of 6617 m(3) 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 km(2)) 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.

This study proposes a seismic reliability–based water distribution system (WDS) optimal design model that minimizes total cost and maximizes seismic reliability. Here, seismic reliability is defined as the ratio of the available quantity of water to the required demand under stochastic earthquake events. A new evaluation model is used to assess seismic reliability, while a multiobjective harmony search (MOHS) based on a ranking approach is used for optimization. The Anytown network was modified for the demonstration of the proposed method. First, this study performs the sensitivity analysis of MOHS parameter values [i.e., harmony search consideration rate (HMCR) and pitch adjustment rate (PAR)] to identify the best parameter set in a pipe-sizing problem of an Anytown network. Then, Pareto optimal solutions with three different tank configurations are obtained and compared with respect to the final Pareto fronts and the system designs. For the sensitivity analysis, it reveals that higher PAR and lo...