Journal of Water Resources Planning and Management

This study investigates the use of global sensitivity analysis as a screening tool to reduce the computational demands associated with multiobjective design and rehabilitation of water distribution systems (WDS). Sobol’s method is used to screen insensitive decision variables and guide the formulation of reduced complexity WDS optimization problems (i.e., fewer decision variables). This sensitivity-informed problem decomposition dramatically reduces the computational demands associated with attaining high-quality approximations for optimal WDS trade-offs. This study demonstrates that the results for the reduced-complexity WDS problems can then be used to precondition and significantly enhance full search of the original WDS problem. Two case studies of increasing complexity—the New York Tunnels network and the Anytown network—are used to demonstrate the proposed methodology. In both cases, sensitivity analysis results reveal that WDS performance is strongly controlled by a small proportion of decision variables, which should be the focus of preconditioning problem formulations. Sensitivity-informed problem decomposition and preconditioning are evaluated rigorously for their ability to improve the efficiency, reliability, and effectiveness of multiobjective evolutionary optimization. Overall, this study reveals for the first time that the use of global sensitivity analysis is computationally efficient and potentially critical when solving the complex multiobjective WDS problems.

For the past three decades, a number of studies have been dedicated to water-distribution system (WDS) optimal design using alternative optimization algorithms. Many of those, however, focused on the introduction and application of new optimization techniques. Application systems optimized in previous studies are generally limited to simple transmission networks, so-called benchmark systems, in which local distribution lines were mostly excluded. Efforts seeking logical approaches to solve complex problems with large number of decisions are lacking. In this paper, logical and efficient approaches that could be utilized to optimize real-life scale WDS by the aid of existing optimization techniques are presented. This study aimed two main objectives: first, the effect of local distribution lines in final system design is investigated, and second, a heuristic to improve the efficiency of meta-heuristic search methods is proposed. Applications to real WDS demonstrate that (1) by integrating the transmission and distribution scales in optimization model, oversizing the transmission system could be avoided and the capacity of local distribution pipes could be appropriately evaluated, and (2) a proposed heuristic is logical and improves optimization performances, and is easily transferrable to any type of random search algorithms. Other issues related to solving the design problem facing engineers are raised and research directions are proposed.

For large water-distribution systems fully detailed models result in a substantial amount of data, making it difficult to manage, monitor, and understand how the main structure of the system works. A possible way to cope with this difficulty is to gain insight to the system behavior by simplifying its operation through topological/connectivity analysis. The objective of this study is to develop and demonstrate a generic topological-based scheme to aid in the analysis of water-distribution systems. The methodology relies on clustering, which divides the distribution system into strongly and weakly connected sub-graphs using the depth first search (DFS) and breadth first search (BFS) graph algorithms. The partitioning results in a connectivity matrix that represents the interconnections between clusters, which can support, for example, a response modeling plan in case of a contamination intrusion incident. A detailed illustrative example and a real complex water-distribution system are explored for demonstrating the developed model capabilities. Possible applications of the proposed algorithm are suggested.

A contaminant source identification (CSI) methodology for water distribution systems is intended to identify possible events (i.e., intrusion nodes, times, duration, and mass rate). The methodology has to be both rapid and able to incorporate uncertainties when identifying possible intrusion nodes (PINs). Identification of PINs has two major issues: the false-negative rate (failure to identify the true ingress location) and the false-positive issue (falsely identifying a location that is not the true ingress location). A data-mining procedure is described and applied, which involves mining an offline-built database to select PINs that possess first-detection times within $\pm m$ from the online sensor first-detection time, with $m$ selected to address issues of false negatives and positives. This data-mining approach is made possible through the power of parallel computing, which demonstrates huge potential by simulating scenarios simultaneously. In the case studies, scenario simulation times are reduced linearly to the number of processors applied. Results show that increasing the number of scenarios in the database can provide input to compute the $m$ value, always reduces the false-negative rate of each sensor, and usually reduces the number of false PINs. Demonstrated by data-mining online application for two case studies of water distribution systems, the procedure identifies the PINs within less than 4 min, demonstrating that data mining represents a rapid and efficient CSI procedure.

During the past few years, issues concerning sustainable management of water distribution systems have attracted interest through an integrated policy aimed at reducing leakage through a pressure management strategy. Pressure reducing valves (PRVs) are often used in water networks to prevent the downstream hydraulic grade from exceeding a set value, although they must be adequately located to maximize their effectiveness. In recent years, the application of turbines or pumps operating as turbines (PATs) appeared as an alternative and sustainable solution to control network pressure and produce energy. In the present paper, PRVs and PATs were used within a district in a Naples’ water distribution network and showed large potential revenues and an attractive capital payback period.

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, São Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of $3.3\mathrm{L}/\mathrm{s}$ and $2.8\mathrm{L}/\mathrm{s}$ for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of $3.1\mathrm{L}/\mathrm{s}$ and $3.0\mathrm{L}/\mathrm{s}$ for training and test set respectively, which represented about 12% of average consumption.

Construction of huge hydropower plants in the southern region of China has been rapidly increasing in recent years. These plants usually have multiple vibration zones of high head that have a great effect on short-term scheduling and real-time operations. This paper presents a novel approach for optimizing short-term scheduling of large-scale cascaded hydropower systems with multivibration zones of high head. For the purpose of cutting down peak loads, standard deviation minimization relevant to the remaining load series for thermal systems was chosen as the objective nonlinear function. Before the optimization, unit forbidden operation zones were identified by assembled mathematical techniques and hydro unit commitments were optimized using dynamic programming. The combined sets of forbidden operation zones and hydro unit commitments were repeatedly used during the search process. An optimization framework that combined the progressive optimality algorithm with a vibration zone avoidance strategy was finally presented to solve the short-term hydropower scheduling problem. The proposed methodology was applied to a case study in China and the results obtained indicate that it is able to not only handle complex constraints of multivibration zones, but also provide efficient and feasible solutions for short-term scheduling of large plants.

This paper proposes the use of option games as a tool to evaluate trade-offs between flexibility and strategic commitment in industrial water infrastructure projects. Option games meet the challenge of balancing competitive pressure to commit to big-budget projects against a more flexible approach that keeps investment options open. This hybrid approach, which combines real options and game theory, overcomes the shortcoming of traditional investment valuation methods, such as net present value or real option analysis, which do not resolve the challenge. The option games are enumerated by combining a real-options binomial tree and a payoff matrix under conditions of two investment competitors with four strategic scenarios. The calculated results are compared with traditional net present value and real option analysis to show the final value of investment. The application methodology is illustrated through the case of Government of Thailand (GoT) investments in tap and industrial water supplies and/or the private sector (PS) in recycled-water development. The investments are evaluated under four different strategic scenarios: (1) both invest, (2) GoT invests first, PS waits, (3) PS invests but GoT waits, and (4) both wait. The paper indicates that option games provide a tool that allows decision makers to accurately value all choices with consideration not only of future uncertainties, but also of competitors’ decisions. Policy makers or managers can make rational investment decisions by quantifying the values of commitment and flexibility.

A linked kernel-optimization model for the planning of optimal groundwater development for irrigation is presented. The planning ensures optimization of zonal crop patterns subject to the constraints on the maximum water table depth and the stream-aquifer interflow at the dynamic equilibrium. The model is computationally inexpensive as compared to the traditional linked simulation-optimization models. Its use is demonstrated by applying it to a canal command area in India. Five kernel models are developed relating the maximum water table depth and four critical stream-aquifer interflow rates to the crop areas. The necessary data base is generated by using a physically based precalibrated simulation model of groundwater flow. The kernel models are linked to a genetic algorithm-based optimizer for arriving at the optimal cropping pattern and the associated pumping pattern. The near-optimal solution so obtained is further fine-tuned through an inexpensive application of the linked simulation-optimization model.

Climate predictions with lead times of one season or more often provide prospects for exploiting climate-related risks and opportunities. This motivates the evaluation of precipitation prediction techniques from statistical and dynamical models, and their combination, to potentially augment prediction skill over the Blue Nile Basin in Ethiopia. This work considers to what degree greater skill or reliability in a particular prediction technique translates through hydropower management models given their nonlinear response. One hundred precipitation series from 1981–2000 are generated to compare prediction techniques. The linked multimodel ensemble climate forecast/hydropower system proves superior to the statistical and dynamical prediction technique linked systems across a range of metrics. This includes an expected increase in annual benefits by $4–5–million on average. The climate forecast/hydropower system is sufficiently flexible to allow water managers to attain an optimal balance between benefits and the dependability of energy delivery by varying exceedance probability and target energy thresholds, with the added benefit of forecast guidance. Ideally this provides decision makers with incentives to integrate improved prediction techniques into sectoral management models, and further justifies expanding efforts into climate forecast improvement.

Behavior and technological impacts on residential indoor water use and conservation efforts in the United States are identified. Preexisting detailed end-use data was collected before and after toilets, faucets, showerheads, and clothes washers were retrofitted in 96 owner-occupied, single-family households in Oakland, California; Seattle, Washington; and Tampa, Florida, between 2000 and 2003. Water volume, duration of use, and time of use were recorded and disaggregated by appliance for two weeks before and four weeks after appliances were retrofitted. For each appliance, observed differences in water use before and after retrofits are compared to water savings predicted by simple analytical, regression, and hybrid models. Comparisons identify prediction precision among models. Results show that observed and predicted distributions of water savings are skewed with a small number of households showing potential to save more water. Regression and hybrid model results also show the relative and significant influence on water saved of both technological (flow rates of appliances) and behavioral (length of use, frequency of use) factors. Additionally, regression results suggest the number of residents, performance, and the frequency of appliance use are key factors that distinguish households that save the most water from households that save less. Study results help improve engineering methods to estimate water savings from retrofits and allow water utilities to better target subcategories of households that have potential to save more water.

In planning and optimization studies, engineers are often asked to provide cost estimates for facilities without have knowledge of details of the design. This paper presents a summary of published cost equations for water and wastewater pumping stations with some recommendations for application. In general, because of economies of scale, these cost equations are not linear.