Journal of Water Resources Planning and Management

A direct search method using genetic algorithms (DSGA), which seeks to directly find optimal parameters for prescribed operating policies, is utilized for optimization of multireservoir operational problems and several modifications are presented. The problems presented consist of 3, 7, and 16 reservoirs, respectively, from the Greater Karoon system in Iran. For the three-reservoir problem, the DSGA method is used to obtain optimal linear operating policies and has proven to be very effective in both objective function values and computational time in comparison to the more traditional optimization models based on dynamic programming. However, the model must be modified to optimize larger problems successfully. The first set of proposed modifications is primarily for enhancing the efficiency of the genetic algorithm (GA) used in the model and reducing its sensitivity to GA parameters such as the probability of mutations and size of generations. The more robust modified DSGA is then applied to the seven-reservoir problem to obtain optimal linear policies as well as two forms of piecewise linear ones, which achieves better objective values. The other modifications applied to the model are a Fourier series approximation which defines the seasonal variation of policy parameters and a stepwise GA, which employs varying lengths of simulations for fitness evaluations in different generations. These modifications reduce the time of computations significantly. As a final point, the fine-tuned modified DSGA model optimizes the 16-reservoir problem in less than $20\mathrm{h}$ , which is a significant time period. Computational time is estimated to increase geometrically (second order) with the number of reservoirs.

Decision making under uncertainty is common in design and planning of water resource projects. The implications and effects of uncertainty on the ranking of design alternatives have been investigated in the literature. However, various decision rules are used to rank candidate alternatives under uncertainty, which creates difficulty in choosing the best rule in practice. This paper starts with a description of uncertainty sources in the decision making process, followed by an overview of various decision rules. A water resources management example is then used to evaluate the utility of different decision rules in terms of the ability of explicit quantification of weight uncertainty, nature of rules, complexity, and computational efficiency. This review provides a comprehensive summary of various decision rules for alternative ranking under uncertainty and guides on how to choose an appropriate decision rule that fits the decision makers’ purpose and use.

Although various strategies have been defined for using optimization and simulation techniques in combination, few of these have been employed in real-world multireservoir and multiuse water systems. Despite potential for the use of optimization in the search for efficient alternatives, full integration between simulation and optimization has not yet been achieved to close the gap between research and real-world application. This paper illustrates a procedure integrating optimization and simulation models with the aim of identifying and evaluating drought mitigation measures by means of a proactive approach. In the proposed technique the system manager must define preferences and priorities instead of using the precise operating rules usually required by simulation. A tuning phase is needed when using the proposed approach to evaluate key parameters of the mixed technique for real case application.

Excessive nutrient loads to the Chesapeake Bay cause violations of the new dissolved oxygen water quality standard established to protect the Bay’s living resources. Reducing nitrogen and phosphorus loads is necessary to achieve the dissolved oxygen standard. Based on a set of water quality model runs, a response surface method to establish a function of dissolved oxygen (DO) versus total nitrogen (TN) and total phosphorus (TP) loads was used, which plots as a three-dimensional surface. For a specific criterion for DO, i.e., achievement of the DO standard, a curve of DO versus TN and TP loads that meets the DO criterion can be isolated. Each of the paired TN and TP loads on this trade-off curve results in an equivalent level of DO, but usually at different nutrient reduction costs. This paper explores cost-effective alternatives in nutrient reduction to achieve the DO water quality standard in the deep water designated use of Segment CB4, which is the last and most difficult region for achievement of DO standards in the Chesapeake. This paper analyzes DO response surface plots and nitrogen–phosphorus trade-off curves. The effects of nutrient limitation on algal growth, water clarity, and DO concentrations in two different nitrogen and phosphorus load scenarios are examined to understand the responses of water quality to nitrogen and phosphorus trades.

This paper addresses the problem of optimal pressure management in water distribution systems through the introduction and regulation of pressure reducing valves. Reduction in pressure is aimed at controlling water leakages which, being in some cases a high proportion of the total volume supplied, are nowadays one of the major concerns for water utilities. The determination of the number, location, and setting of such valves is formulated as a two criteria optimization problem and is solved with multiobjective genetic algorithms. In particular, the first criterion is represented by the minimization of the number of valves, and the second is the minimization of the total leakage in the system, when maintaining the required pressure at each node. The great advantage of the multiobjective approach resides in the fact that, in one run, several trade-off alternatives are found, thus providing the set of the optimal solutions with a different level of compromise between the conflicting objectives. At the same time, data necessary for practical choice and operation of pressure reducing valves may be determined.

Droughts, resulting from natural variability in supply and from increased demand due to urbanization, have severe economic implications on local and regional water supply systems. In the context of short-term (monthly to seasonal) water management, predicting these supply variations well in advance are essential in advocating appropriate conservation measures before the onset of drought. In this study, we utilized $3\text{-}\text{month}$ ahead probabilistic multimodel streamflow forecasts developed using climatic information—sea surface temperature conditions in the tropical Pacific, tropical Atlantic, and over the North Carolina coast—to invoke restrictions for Falls Lake Reservoir in the Neuse River Basin, N.C. Multimodel streamflow forecasts developed from two single models, a parametric regression approach and semiparametric resampling approach, are forced with a reservoir management model that takes ensembles to estimate the reliability of meeting the water quality and water supply releases and the end of the season target storage. The analyses show that the entire seasonal releases for water supply and water quality uses could be met purely based on the initial storages (100% reliability of supply), thereby limiting the use of forecasts. The study suggests that, by constraining the end of the season target storage conditions being met with high probability, the climate information based streamflow forecasts could be utilized for invoking restrictions during below-normal inflow years. Further, multimodel forecasts perform better in detecting the below-normal inflow conditions in comparison to single model forecasts by reducing false alarms and missed targets which could improve public confidence in utilizing climate forecasts for developing proactive water management strategies.

This paper presents a study on a response surface-based optimization approach for soil vapor extraction system (SVES) design. The SVES design involves extraction rate determination for a number of wells at fixed locations. Two optimization problems were investigated in this study, including: (1) minimizing total cost while achieving the required cleanup goal; and (2) maximizing percentage of contaminant mass removal under the constraint of total extraction rate. The response surface-based optimization approach integrates a multiphase flow and transport simulator, a genetic algorithm and a response surface method that employs regression analysis for constructing an approximation function of mass removal percentage in terms of extraction rate. A symmetric Latin hypercube design (SLHD) or factorial design plan is employed to obtain a moderate number of design points for different optimization problems. Besides, an iterative optimization procedure is implemented in the approach to enhance the accuracy of the approximated response function by gradually adding more design points to the design space. Results show that the response surface-based optimization approach is superior to a simulation/optimization approach in terms of optimal solution and computation time.