Journal of Water Resources Planning and Management

Isolation valves are crucial components of water distribution systems for separating pipe segments from the network for repair or maintenance purpose. This paper looks at the impacts of isolation valve failure on three indictors, including number of valves needed to isolate a distribution system segment, size of distribution system segments, and shortfall in meeting demands during failure. A network with various isolation valve configurations in terms of valve density is used as a case study. The results obtained from the case study show that the failure of an isolation valve has substantially varying impacts on system performance during a shutdown. The valve density in the network determines the impacts of inoperable valves on a shutdown. Generally speaking, a higher density of isolation valves leads to the less impact of valve failure. Finally, several conclusions drawn from the critical valve analysis in this study can be applied to guide isolation valve maintenance and management.

This paper proposes a fuzzy multiobjective programming model for meeting competing objectives in the optimal design of water distribution systems (WDSs). Fuzzy membership functions for minimizing the pipe network cost and maximizing a number of reliability surrogates are defined, and the model maximizes the degree of satisfaction of these membership functions. Reliability surrogates investigated include: (1) the sum over all nodes of the relative surplus of energy at each node; (2) the minimum surplus head at a critical node; (3) the sum over all nodes of the relative surplus of energy at each node modified by the degree of uniformity of its associated loop; and (4) the minimum uniformity of the associated loops over all nodes. The model is applied to various combinations of objective functions, to identify the design pipe diameters of the water-main network of Farhadgerd, Iran. Optimal solutions of the various model variations, based on inclusion of one or more reliability surrogate(s), show that for this WDS the third surrogate (3) may be a reasonable substitute for the first surrogate (1), and that while the inclusion of the last surrogate (4) may be beneficial, the inclusion of the second surrogate (2) may not. Based on postoptimization cut-set analyses, the model that minimizes cost and maximizes surrogates (3) and (4) exhibits the highest level of mechanical reliability, while the model that minimizes cost and maximizes the second surrogate (2) exhibits the lowest level of mechanical reliability.

In this paper, an index-based approach is proposed to locate chlorine booster stations in a water distribution network (DN). Chlorination is a common practice for secondary disinfection, essential for protection against microbiological regrowth and contaminant intrusion. However, higher levels of free residual chlorine (FRC) may lead to unwanted disinfectant by-products (DBPs) and taste and odor complaints. Booster chlorination can be used to maintain minimum levels of FRC. This requires proper selection of booster locations. The approach proposed here uses a generic hydraulic and water quality modeling software programmers’ toolkit to predict FRC and total trihalomethanes (TTHMs) converted, thereafter, into a water quality index (WQI). A heuristic algorithm maximum covering location problem (MCLP) used in optimization maximizes WQI. TTHM is converted into trihalomethane (THM) species using quadratic optimization, then to cancer and noncancer risk potentials. Finally, the required number of booster stations is recommended based on a trade-off analysis of risk potentials, WQI, and the life cycle cost of booster chlorination. For proof of concept, case studies are conducted in a hypothetical and a municipal DN.

Optimal rehabilitation of large water distribution systems (WDSs) with many decision variables is often time consuming and computationally expensive. This paper presents a new optimal rehabilitation methodology for WDSs based on the graph theory clustering concept. The methodology starts with partitioning the WDS based on its connectivity properties into a number of clusters (small subsystems). Pipes that might have direct impact on system performance are identified and considered for the rehabilitation problem. Three optimization-based strategies are then considered for pipe rehabilitation in the clustered network: (1) rehabilitation of some of the pipes inside the clusters, (2) rehabilitation of pipes in the path supplying water to the clusters, and (3) a combination of Strategies 1 and 2. In all optimization strategies, the decision variables for the rehabilitation problem are the diameters of duplicated pipes; the objective functions are to minimize the total cost of duplicated pipes and the number of nodes with pressure deficiency. The performance of proposed strategies was demonstrated in a large WDS with pressure deficiencies. The performance of these strategies was also compared to the full search space optimization strategy and engineering judgment–based optimization strategy in which all pipes and selection of pipes are considered as decision variables, respectively. The results show that Strategy 3 is able to generate solutions with similar performance that are cheaper by around 53% and 35% in comparison with the full search space and engineering judgment–based optimization strategies, respectively. The results also demonstrate that the cluster-based approach can reduce the computational efforts for achieving optimum solutions compared to the other optimization strategies.

The application of multiobjective evolutionary algorithms (MOEAs) to sanitary sewer overflow (SSO) optimization problems typically requires multiple runs of a simulation model and can be very computationally expensive. There is a need for simulation-optimization models that use fewer functional evaluations of the hydraulic model to identify near optimal solutions. In this study, two conflicting objectives were analyzed: maximizing SSO reduction and minimizing rehabilitation cost. This paper introduces a novel MOEA, the enhanced nondominated sorting evolution strategy (eNSES) that uses a specialized operator to guide the algorithm toward known SSOs locations. This strategy is being tested in an existing network in the eastern San Antonio Water System network. It has been compared with NSGA-II and NSES based on hypervolume and the overall nondominated vector generation ratio (ONVGR). The results show that eNSES improves the convergence rate by approximately 70% over the tested alternative algorithms, performing as well as NSGA-II and outperforming NSES in terms of the hypervolume by nearly 10%. In terms of the ONVGR, eNSES performed similarly to NSES but outperformed NSGA-II by 42%.

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 is the system’s ability to recover from flooding or inundation damage. A novel pump operating method is proposed in this paper as a nonstructural measure, while expansion of the discharge pump capacity is considered to be a structural measure. The proposed method is demonstrated by application to the Daerim sewer network in Seoul, Korea, which has a discharge pump located in the detention reservoir at the center of the study network. The results show that less flooding is predicted with the new operating method than with the current operating method. Additionally, the new operating method, in conjunction with a pump capacity reflecting the inflow into the detention reservoir, yields stable results in most operating scenarios. The results of the resilience analysis illustrate that the new operating method applied in conjunction with sufficient pump capacity is more effective than the current operating method.

Pareto archived dynamically dimensioned search (PA-DDS) is one of the meta-heuristic methods available to solve multiobjective reservoir operation problems. This study uses this method to optimize reservoir operation rules with the objectives of maximizing the power generation and water supply. The performance of PA-DDS is compared with the nondominated sorting genetic algorithm–II (NSGA-II) in terms of a hypervolume indicator and the distribution of optimized nondominated solutions (NDSs) in a case study of Hanjiang cascade reservoirs in China. The results indicate that PA-DDS can increase the amount of power generation and water supply, respectively. Moreover, the uncertainty in reservoir operation optimized by both methods is analyzed in terms of the NDS distribution and the trade-off relationship between water supply and power generation. The results demonstrate that PA-DDS outperforms NSGA-II not only in the Pareto front approximation (NDS), but also with an increase in water supply by about $300\mathrm{million}{\mathrm{m}}^3/\mathrm{year}$ for Hanjiang cascade reservoir operation.

Efficient management of a sewer system includes the control of the conveyed wastewater quality to adequately operate treatment plants and protect the receiving water bodies. Moreover, these systems are vulnerable to either accidental spills or intentional unauthorized discharges. To properly manage them, a limited number of sensors could be placed at different locations to monitor the water quality. In this paper, multiobjective and single-objective optimization procedures to optimally locate sensors in sewer systems are proposed, tested, and compared. The multiobjective procedures include objective functions related to information theory (IT procedure), detection time and reliability (DR procedure), and a combination of them (IT_DR procedure). The single-objective procedures include a greedy-based objective function (GR procedure) and a merged objective function (DR_IT_GR procedure). The procedures show a similar performance when applied on a small network, whereas in a real system, the results show that (1) the IT-based method can be effectively used as a filtering technique; (2) the DR_IT_GR procedure outperforms the other multiobjective ones; and (3) the GR procedure is very efficient in finding the Pareto extreme solutions.

Irrigation is the major user of total water use in most of the tropical countries located in Southeast Asia. Therefore, knowledge on future changes in irrigation demand in the context of climate change is very important for long-term planning and management of water resources of the region. This paper investigated the changes in irrigation water demand in an intensive irrigated area of Malaysia under climate change scenario. Global circulation models (GCMs) outputs are downscaled using statistical downscaling model (SDSM) to project the future changes in rainfall and temperature at local scale. The irrigation water demand estimation model known as CROPWAT is calibrated and validated with historical data and then used to project the future changes in irrigation demand under SDSM projected climatic condition. The results show that temperature and rainfall in the area will increase by 0.2° C and 4% per decade, respectively over the time period 2020–2099. The changes in climate will reduce irrigation water demand at a rate of 0.9% per decade in the area. However, the irrigation demand will be much more variable in future, and therefore, the major challenge in water resources management will be to handle the uncertainty. It is expected that the seasonal and annual variations in irrigation demand projected by the study will help in operating reservoirs and sustainable management of water resources in the region in the context of climate change.