Journal of Water Resources Planning and Management

A network of reservoirs and diversion structures in the Comoé River Basin in southwestern Burkina Faso, West Africa, provides municipal water supply and irrigation for sugarcane agribusiness and downstream farmers. The region is characterized by severe intraseasonal and inter-annual variability of precipitation and reservoir inflows. Reservoir operations are generally conservative, even during relatively wet years. A stochastic decision support tool (DST) is described which translates a scenario-tree of streamflow and precipitation forecasts, conditionally weighted by a tercile seasonal precipitation forecast, into optimal release schedules for the upcoming year. It is shown that the DST coupled with the seasonal precipitation forecasts may provide more efficient and equitable release decisions that benefit the variety of stakeholders in the basin.

To optimize regional economies, social employment, and water quality protection, a multiobjective bilevel optimization model under a fuzzy random environment based on constructed wetland planning is developed in this study. Fuzzy random variables are used to describe the uncertainties in the system, and three special fuzzy random simulation methods are proposed to address the fuzzy random variable calculations. From the inherent bilevel model interaction, a new algorithm called the fuzzy random simulation-based nested genetic algorithm (FRS-based NGA) is designed as an intelligent solution to solve the model. Then, the model is applied to a real-world case: the Chaohu Lake watershed, China. The results under different objective values, probability levels, and possibility levels are compared so that an optimal scheme for regional economic development, social employment, and the wetland construction scale can be identified. Finally, the comparative analysis, sensitivity analysis, and convergence analysis are provided to illustrate the effectiveness of the proposed model and algorithm.

A reliable forecasting model for daily water consumption would provide the data basis for scheduling urban water supply facilities. In this paper, a variable-structure support vector regression (VS-SVR) model is developed for dynamic forecast of the water consumption. Considering the nonlinear mapping capability of the SVR, the next-day water consumption is associated with the past water consumption series using the SVR model. To better accommodate the dynamic characteristics, the model structure of the SVR is variable in response to the receding horizon of the water consumption series. The variable model structural parameters are obtained using an extended Kalman filter (EKF) as the feedback correction tool. Combining the robustness of the model predictive control framework and the nonlinearity of the SVR, the proposed VS-SVR model is a dynamic approach to forecasting daily urban water consumption, evaluated using real data collected from a water company from January 2010 to December 2011. Compared with the SVR model, the dynamic forecast of daily urban water consumption using the proposed VS-SVR method improves the one-day-ahead forecast mean absolute error by $\mathrm{2,637}{\mathrm{m}}^3/\mathrm{d}$ (1.2% mean absolute percentage error). The results show that the dynamic update is better, at least in a global sense.

This paper shows how pipe replacements and control valve installations can be optimized in water distribution networks to reduce leakage, under minimum nodal pressure constraints. To this end, a hybrid multiobjective algorithm, which has pipe diameters and valve positions and settings as decisional variables, was set up. The algorithm also enables identification of the isolation valves that have to be closed in order to improve effectiveness of the control valves installed. The algorithm is initially applied to the optimal valve location problem, where it explores the trade-off between the number of installed control valves and the daily leakage volume. In this context, the analysis of the results proves the new algorithm more effective than a multiobjective genetic algorithm widely adopted in the scientific literature. Furthermore, it shows that if some isolation valves identified ad hoc are closed in the network, the installation of control valves determines larger leakage volume reductions. In a second application of the algorithm, pipe replacements and control valve installations are simultaneously performed. In this case, a Pareto front of trade-off solutions between installation costs and daily leakage volume is obtained. For the choice of the final solution within the front, an economic criterion based on the long-term convenience analysis is also illustrated.

Various multiobjective evolutionary algorithms (MOEAs) have been applied to solve the optimal design problems of a water distribution system (WDS). Such methods are able to find the near-optimal trade-off between cost and performance benefit in a single run. Previously published work used a number of small benchmark networks and/or a few large, real-world networks to test MOEAs on design problems of WDS. A few studies also focused on the comparison of different MOEAs given a limited computational budget. However, no consistent attempt has been made before to investigate and report the best-known approximation of the true Pareto front (PF) for a set of benchmark problems, and thus there is not a single point of reference. This paper applied 5 state-of-the-art MOEAs, with minimum time invested in parameterization (i.e., using the recommended settings), to 12 design problems collected from the literature. Three different population sizes were implemented for each MOEA with respect to the scale of each problem. The true PFs for small problems and the best-known PFs for the other problems were obtained. Five MOEAs were complementary to each other on various problems, which implies that no one method was completely superior to the others. The nondominated sorting genetic algorithm-II (NSGA-II), with minimum parameters tuning, remains a good choice as it showed generally the best achievements across all the problems. In addition, a small population size can be used for small and medium problems (in terms of the number of decision variables). However, for intermediate and large problems, different sizes and random seeds are recommended to ensure a wider PF. The publicly available best-known PFs obtained from this work are a good starting point for researchers to test new algorithms and methodologies for WDS analysis.

Groundwater remediation designs rely on simulation models that are subjected to various sources of uncertainty. This study introduces a hierarchical Bayesian model averaging (HBMA) method to segregate sources of uncertainty in a hierarchical order and conduct comparative evaluation of BMA models for remediation designs. A BMA tree of models is developed to understand the impact of individual sources of uncertainty in the remediation design. The HBMA method is applied to chance-constrained (CC) formulation for an aquifer remediation design that aims to reduce concentration at a selected control point using scavenger wells. Thirty-six (36) flow and transport models for concentration prediction are developed to analyze the impact of three sources of uncertainty on the remediation design. An essential step in HBMA is to calculate posterior model probabilities as model weights. The best simulation model has the highest model weight. The results show that although using the best simulation model requires the least pumping rate for the scavenger well, it underestimates prediction variances of concentration. The scavenger well pumping rate increases as more sources of uncertainty are considered. The HBMA enables the chance-constrained formulation to consider both model parameter and model structure uncertainties for aquifer remediation designs. The contributions of prediction variance from individual sources of uncertainty to the total prediction variance can be evaluated, which provides an understanding of the impact of individual sources of uncertainty and their corresponding propositions on remediation designs.

In this paper the authors propose a global–local methodology for optimizing the short-term operation of hydroelectric plants. The authors determine the tradeoffs between minimizing the daily release from the plant and minimizing the number of startups and shutdowns of the generating units. The model is formulated as a mixed integer, nonlinear programming optimization problem with multiple objectives. The authors consider the nonlinearities of the generating units without simplifications or approximations. The authors develop a solution method that combines an evolutionary algorithm for the global search of the integer variables and a gradient-based local optimizer for the continuous variables. The local optimizer is embedded in the global search algorithm. Convergence is achieved by iterating between the global search and the local optimizer. The proposed methodology is applied to a moderately sized Brazilian hydroelectric plant that belongs to the national interconnected system. Additionally, a comparative study was conducted using historical operational records. The results demonstrate that the proposed methodology is feasible for online daily operations and delivers two specific benefits. The first is the efficiency gained, as the model seeks to operate the generating units as close as possible to their most efficient operating points. The second benefit is reduction of the units’ maintenance costs, as the model minimizes switching on/off of the generating units.