Ranji S. Ranjithan

Battle of the Water Networks II

The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions related to the design and operation of water distribution systems (WDSs) undertaken within the Water Distribution Systems Analysis (WDSA) Symposium series. The BWN-II problem specification involved a broadly defined design and operation problem for an existing network that has to be upgraded for increased future demands, and the addition of a new development area. The design decisions involved addition of new and parallel pipes, storage, operational controls for pumps and valves, and sizing of backup power supply. Design criteria involved hydraulic, water quality, reliability, and environmental performance measures. Fourteen teams participated in the Battle and presented their results at the 14th Water Distribution Systems Analysis conference in Adelaide, Australia, September 2012. This paper summarizes the approaches used by the participants and the results they obtained. Given the complexity of the BWN-II problem and the innovative methods required to deal with the multiobjective, high dimensional and computationally demanding nature of the problem, this paper represents a snap-shot of state of the art methods for the design and operation of water distribution systems. A general finding of this paper is that there is benefit in using a combination of heuristic engineering experience and sophisticated optimization algorithms when tackling complex real-world water distribution system design problems

Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework

Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end-of-season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no-transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system.

Conjunctive Use of Models to Design Cost-Effective Ozone Control Strategies

Abstract The management of tropospheric ozone (O3) is particularly difficult. The formulation of emission control strategies requires considerable information including: (1) emission inventories, (2) available control technologies, (3) meteorological data for critical design episodes, and (4) computer models that simulate atmospheric transport and chemistry. The simultaneous consideration of this information during control strategy design can be exceedingly difficult for a decision-maker. Traditional management approaches do not explicitly address cost minimization. This study presents a new approach for designing air quality management strategies; a simple air quality model is used conjunctively with a complex air quality model to obtain low-cost management strategies. A simple air quality model is used to identify potentially good solutions, and two heuristic methods are used to identify cost-effective control strategies using only a small number of simple air quality model simulations. Subsequently, the resulting strategies are verified and refined using a complex air quality model. The use of this approach may greatly reduce the number of complex air quality model runs that are required. An important component of this heuristic design framework is the use of the simple air quality model as a screening and exploratory tool. To achieve similar results with the simple and complex air quality models, it may be necessary to “tweak” or calibrate the simple model. A genetic algorithm-based optimization procedure is used to automate this tweaking process. These methods are demonstrated to be computationally practical using two realistic case studies, which are based on data from a metropolitan region in the United States.

Special Issue on the Role of Systems Analysis in Watershed Management

Watersheds are coupled human-natural systems (CHNSs) characterized by interactions between human activities and natural processes crossing a broad range of spatial and temporal scales. As stressed by a National Research Council (NRC) report (1999), watershed management poses an enormous challenge in the coming decades. The USDA and the EPA adopted a watershed approach to manage watersheds considering the interdependence among human, abiotic, and biotic components and the feedbacks that arise among management practices and their socioeconomic and environmental consequences. Concurrently, the attention of the environmental and water resources systems research community has evolved from the management of individual reservoirs, storm water, and aquifer systems to more integrated watershed or river basin systems. The application of systems analysis tools including simulation, optimization, and their integration offers an analytical mindset and a diversity of tools capable of addressing the complex challenges, which arise from human-natural interactions as well as communicating subsequent analyses to decision makers. Methods of systems analysis have been integral to water resources systems planning and management since the 1960s. Initially, methods of simulation, mathematical programming, and decision analysis borrowed from the field of operations research were applied to water management challenges. Later, in the 1990s, innovations in complex systems arising, in part, from previous contributions from catastrophe theory in the 1970s and chaos theory in the 1980s began to be applied to the field of water resources planning and management. Today, the application of all of these methods that are termed a systems approach remains critical to our field. Perhaps now more than ever before, systems methods are needed to solve watershed management problems due to the emergence of numerous new concerns relating to stakeholder participation, environmental ethics, life-cycle analysis, sustainability, industrial ecology, and design for ecological (as opposed to engineering) resilience (Dobson and Beck 1999). Both practitioners and researchers routinely face watershed management challenges, including, for example, restoring degraded ecosystems to achieve a balance between human and nature, resolving conflicts over protection of open space and environmental quality and development interests, and more generally accommodating within a watershed context water requirements for food, energy, and environment. Addressing these and other challenges requires the development of innovative systems concepts, methods, and algorithms for effective watershed management that can lead to both socioeconomic and environmental sustainability. Recent scientific, technological, and institutional developments have already and will continue to facilitate integrated watershed systems analysis approaches. We expect innovations relating to a wide range of emerging areas to continue facilitating development of watershed systems analysis including, but not limited to (1) distributed watershed hydrologic modeling and digital watersheds facilitated by hydro-informatics with improved forecast capacity; (2) increasing availability of distributed and digital datasets [e.g., remote sensing, sensor-based monitoring, and cyberinfrastructure (CI)]; (3) multidisciplinary research efforts among hydrologists, ecologists, economists, systems experts, and others; (4) institutional and financial support for watershed restoration practices; (5) improvements in computational and optimization algorithms; and (6) evolution in our ability to integrate ecological, environmental, and social objectives into what was once only a more narrow economic analysis (Lund and Cai 2006). Perhaps the most important developments of all relating to the application of water resources systems methods involve advances in computational sciences that have made possible more advanced quantitative analyses and have moved research more broadly into modeling of a watershed or a river basin as an integrated system of, e.g., reservoirs, aquifers, wetlands, and drainage systems. The goal of this special issue is to publish a representative set of papers focused on the field of watershed management modeling [see Zoltay et al. (2010)], which embraces and extends the myriad of recent advances described previously. This special issue is expected to serve the water resources management and planning community by highlighting the current state of some innovative research findings relating to applications of systems methods for solving various watershed management modeling problems. These problems include nonpoint source pollution management in urban or rural watersheds (papers by Jacobi et al., McGarity, Woodbury and Shoemaker, and Limbrunner et al.), water supply (paper by Giacomoni et al.), water allocation (papers by Riegels et al. and Pulido-Velazquez et al.), flood control (paper by Karamouz and Nazif), best management practices (BMPs) design and placement (papers by McGarity, Limbrunner et al., and Karamouz and Nazif), climate change adaptations (papers by Woodbury and Shoemaker and Karamouz and Nazif), total maximum daily load (TMDL) policy assessment (papers by Mirchi and Watkins and McGarity), and watershed system operations (papers by Anghileri et al. and Muste et al.). These problems are addressed through a number of real-world case studies, including both U.S. and international applications. Interestingly, a number of specific suggestions for policy and engineering design and system operations that arise from these case study problems are provided. This set of papers also demonstrates the application of the state-of-the-art systems techniques to analyzing watershed management modeling problems. Classic linear, nonlinear, and dynamic programming models are still useful and exhibit potential for

Enhanced Simulation-Optimization Approach Using Surrogate Modeling for Solving Inverse Problems

This study investigates and discusses groundwater system characterization problem utilizing surrogate modeling. In this inverse problem, the contaminant signals at monitoring wells are recorded to recreate the pollution profiles. In this study, simulation-optimization approach is a technique utilized to solve inverse problems by formulating them as an optimization model, where evolutionary computation algorithms are used to perform the search. In this approach, the partial differential equations (PDE) groundwater transport simulation model is solved iteratively during the evolutionary search, which in general can be computationally expensive since thousands of simulation model evaluations will be evaluated. To overcome this limitation, the simulation model is replaced by a surrogate model, which is computationally much faster than the simulation model and yet is relatively accurate. Artificial neural networks (ANN) is used to construct surrogate models that provide acceptable accuracy performances. The ANN surrogate model, which replaces the PDE groundwater transport simulation model, is then coupled with a genetic algorithm (GA) search procedure to solve the source identification problem. The results will present the quality solution of the ANN surrogate model versus the groundwater simulation model, the solution of the inverse problem for different experiment scenarios and finally a timing study analysis conducted to measure the surrogate model performance.

Role of multimodel combination and data assimilation in improving streamflow prediction over multiple time scales

In hydrologic modeling, various uncertainty sources may arise due to simplification/representation of real-world spatially distributed processes into the modeling framework, such as uncertainty due to model structure, initial conditions and input errors. One approach that is currently gaining attention to reduce model uncertainty is by optimally combining multiple models. The rationale behind this approach is that optimal weights could be derived for each model during the model combination process so that the developed multimodel predictions will result in improved predictability. Another approach—data assimilation—is gaining popularity in reducing uncertainty by deriving updated initial conditions recursively from the current available observations to reduce overall uncertainty by minimizing the error covariance matrix of state variables. In this paper, an experimental design is proposed to test the performance of both approaches, multimodel combination and data assimilation, in improving the hydrologic prediction at daily and monthly time scales. The experimental design is constructed on a synthetic basis such that the ‘true’ model structure and streamflow values are known. We evaluated the performance of multimodel combination and data assimilation through the experimental design at monthly and daily time scales, then compare how uncertainty due to initial conditions and hydrologic model can be dominant at the respective time scales. For the multimodel combination, we combined the models by evaluating the model performance conditioned on the predictor state. For data assimilation, the Ensemble Kalman Filter (EnKF) was adopted to test its usefulness through the same experimental design. Results from the synthetic study showed that under increased model uncertainty, the multimodel algorithm consistently performed better than the single model predictions and the EnKF algorithm in terms of all performance measures at monthly time scale. However, under daily time scale, the multimodel algorithm did not performing better than the EnKF algorithm in most of the model uncertainty cases. Findings from the synthetic study was also consistent upon application in predicting streamflow at daily and monthly time scales for a watershed in North Carolina.

A Monitoring Network Design Procedure for Three-Dimensional (3D) Groundwater Contaminant Source Identification

Finding the location and concentration of contaminant sources is an important step in groundwater remediation and management. This discovery typically requires the solution of an inverse problem. This inverse problem can be formulated as an optimization problem where the objective function is the sum of the square of the errors between the observed and predicted values of contaminant concentration at the observation wells. Studies show that the source identification accuracy is dependent on the observation locations (i.e., network geometry) and frequency of sampling; thus, finding a set of optimal monitoring well locations is very important for characterizing the source. The objective of this study is to propose a sensitivity-based method for optimal placement of monitoring wells by incorporating two uncertainties: the source location and hydraulic conductivity. An optimality metric called D-optimality in combination with a distance metric, which tends to make monitoring locations as far apart from each other as possible, is developed for finding optimal monitoring well locations for source identification. To address uncertainty in hydraulic conductivity, an integration method of multiple well designs is proposed based on multiple hydraulic conductivity realizations. Genetic algorithm is used as a search technique for this discrete combinatorial optimization problem. This procedure was applied to a hypothetical problem based on the well-known Borden Site data in Canada. The results show that the criterion-based selection proposed in this paper provides improved source identification performance when compared to uniformly distributed placement of wells.

Grid-Enabled Simulation-Optimization Framework for Environmental Characterization

Many engineering and environmental problems that involve the determination of unknown system characteristics from observation data can be categorized as inverse problems. A common approach undertaken to solve such problems is the simulation-optimization approach where simulation models are coupled with optimization or search methods. Simulation-optimization approaches, particularly in environmental characterization involving natural systems, are computationally expensive due to the complex three-dimensional simulation models required to represent these systems and the large number of such simulations involved. Emerging grid computing environments (e.g., TeraGrid) show promise for improving the computational tractability of these approaches. However, harnessing grid resources for most computational applications is a nontrivial problem due to the complex hierarchy of heterogeneous and geographically distributed resources involved in a grid. This paper reports and discusses the development and evaluation of ...

LASSO: a grid-enabled simulation optimization framework

In this paper, we report our experiences developing a grid-enabled framework for solving environmental inverse problems. The solution approach taken here couples environmental simulation models with global search methods and requires the readily available computational resources of the grid for computational tractability. We present a set of results for a ground water release history reconstruction problem, and report significant performance improvements observed for a deployment of the application on the TeraGrid.

Development of a simultaneous search-based pilot point method for subsurface characterization

Strategically applied geo-environmental clean-up methods require a better groundwater flow and transport model. Hydraulic conductivity of the subsurface is one of great sources of uncertainty of this model. In order to search hydraulic conductivities, the simultaneous search-based pilot point method (SSBM) was developed to reduce computational procedure of pilot point method and increase characterization accuracy using a global optimization tool (genetic algorithm). SSBM searches pilot point locations and hydraulic conductivities at selected pilot points simultaneously. In the four different scenarios, the comparison between random pilot point locations and SSBM showed that SSBM produced less than two orders magnitude differences in terms of average of minimum fitness for thirty trials (e.g. 4.05E−02 for scenario 2). With respect to average minimum fitness and average hydraulic conductivity difference, SSBM was comparable to D-optimality based pilot point method (DBM). SSBM produced lower average minimum fitness values and similar average hydraulic conductivity difference but it had more variance. Through these results, SSBM showed the potential to replace the DBM through reduced computational procedures in sensitivity calculation with consideration of variance minimization.