Emily M. Zechman

State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management

During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, ...

Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions

This article proposes to solve the oversatu- rated network traffic signal coordination problem us- ing the Ant Colony Optimization (ACO) algorithm. The traffic networks used are discrete time models which use green times at all the intersections throughout the consid- ered period of oversaturation as the decision variables. The ACO algorithm finds intelligent timing plans which take care of dissipation of queues and removal of block- ages as opposed tothe solecost minimization usually per- formed for undersaturation conditions. Two scenarios are considered and results are rigorously compared with solutions obtained using the genetic algorithm (GA), tra- ditionally employed to solve oversaturated conditions. ACO is shown to be consistently more effective for a larger number of trials and to provide more reliable so- lutions. Further, as a master-slave parallelism is possible for the nature of ACO algorithm, its implementation is suggested to reduce the overall execution time allowing the opportunity to solve real-time signal control systems. only a short time, the time to clear the network may be significant. Costs of infrastructure renewal and expan- sion may be cost prohibitive, and, under limited bud- gets, strategies are needed that enhance the mobility

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

An evolutionary algorithm to generate alternatives (EAGA) for engineering optimization problems

Typically for a real optimization problem, the optimal solution to a mathematical model of that real problem may not always be the ‘best’ solution when considering unmodeled or unquantified objectives during decision-making. Formal approaches to explore efficiently for good but maximally different alternative solutions have been established in the operations research literature, and have been shown to be valuable in identifying solutions that perform expectedly well with respect to modeled and unmodeled objectives. While the use of evolutionary algorithms (EAs) to solve real engineering optimization problems is becoming increasingly common, systematic alternatives-generation capabilities are not fully extended for EAs. This paper presents a new EA-based approach to generate alternatives (EAGA), and illustrates its applicability via two test problems. A realistic airline route network design problem was also solved and analyzed successfully using EAGA. The EAGA promises to be a flexible procedure for exploring alternative solutions that could assist when making decisions for real engineering optimization problems riddled with unmodeled or unquantified issues.

Complex Adaptive Systems Framework to Assess Supply-Side and Demand-Side Management for Urban Water Resources

AbstractThe availability of water resources in many urbanizing areas is the emergent property of the adaptive interactions among consumers, policy, and the hydrologic cycle. As water availability becomes more stressed, public officials often implement restrictions on water use, such as bans on outdoor watering. Consumers are influenced by policy and the choices of other consumers to select water-conservation technologies and practices, which aggregate as the demand on available water resources. Policy and behavior choices affect the availability of water for future use as reservoirs are depleted or filled. This research posited urban water supply as a complex adaptive system (CAS) by coupling a stochastic consumer demand model and a water supply model within an agent-based modeling (ABM) framework. Public officials were simulated as agents to choose water conservation strategies and interbasin transfer strategies, and consumers were simulated as agents, influenced by various conservation-based programs to...

Simulation-optimization framework to support sustainable watershed development by mimicking the predevelopment flow regime.

A new approach is presented to achieve a more aggressive sustainability objective for designing transportation infrastructure and land use planning: to design BMPs to continuously mimic the natural flow regime and ensure that ecosystems downstream of development would not be adversely affected. As the land uses are changed for development of urban areas and transportation infrastructure, ecosystems in receiving water bodies are significantly affected by the changes in duration, peak, and minimum flows. Though Best Management Practices (BMPs) are typically designed to not exceed some peak flow during a design storm and perhaps maintain a minimum flow at low-flow periods, downstream conditions are altered, potentially harming ecosystems. A new approach is presented to achieve a more aggressive sustainability objective: to design BMPs to continuously mimic the natural flow regime and ensure that ecosystems downstream of development would not be adversely affected. This objective may not be achievable through the implementation of a single detention pond at a watershed outlet; a system of BMPs strategically placed throughout the watershed may be required. Several BMPs exist as options for treatment, such as detention/retention ponds, constructed wetland systems, infiltration systems (i.e., porous pavement), and vegetative filtrations systems. As each system chosen for implementation must be specified by a set of design decisions and placement location, an efficient mechanism of optimization is needed to handle the large array of decisions. In addition, a comprehensive modeling framework is needed to simulate a collection of BMPs simultaneously. A quantitative analysis framework is described and illustrated for coupling BMP and watershed models with optimization techniques.

Complex adaptive systems approach to simulate the sustainability of water resources and urbanization

AbstractUrban water resources should be managed to meet conflicting demands for environmental health, economic prosperity, and social equity for present and future generations. While the sustainability of water resources can depend on dynamic interactions among natural, social, and infrastructure systems, typical water resource planning and management approaches are based on methodologies that ignore feedbacks and adaptations among these systems. This research develops and demonstrates a new complex adaptive systems approach to model the dynamic interactions among population growth, land-use change, the hydrologic cycle, residential water use, and interbasin transfers. Agent-based and cellular automaton models, representing consumers and policymakers who make land- and water-use decisions, are coupled with hydrologic models. The framework is applied for an illustrative case study to simulate urbanization and the water supply system over a long-term planning horizon. Results indicate that interactions amon...

Simulation-Optimization Approach to Design Low Impact Development for Managing Peak Flow Alterations in Urbanizing Watersheds

AbstractThe process of urbanization transforms natural landscape into impervious land cover, affecting the ecosystem health of receiving water bodies and downstream communities by changing the timing and volumes of the natural flow regime. Best management practices (BMP) and low impact development (LID) are a set of mitigating measures that can be considered for watershed management to mitigate the hydrologic consequences of urbanization. This research develops a methodology to select sites for placing LID technologies, namely rainwater harvesting and permeable pavements, to reduce hydrologic impacts, measured as alterations to the peak flow while meeting a prespecified budget. A simulation-optimization methodology couples a genetic algorithm with a hydrologic model, a hydraulic model, and curve number-based models of LID technologies. The trade-off between costs and peak flow alteration is explored by optimizing LID placement under varying budget constraints. Strategies that combine a detention pond and ...

An adaptive cyberinfrastructure for threat management in urban water distribution systems

Threat management in drinking water distribution systems involves real-time characterization of any contaminant source and plume, design of control strategies, and design of incremental data sampling schedules. This requires dynamic integration of time-varying measurements along with analytical modules that include simulation models, adaptive sampling procedures, and optimization methods. These modules are compute-intensive, requiring multi-level parallel processing via computer clusters. Since real-time responses are critical, the computational needs must also be adaptively matched with available resources. This requires a software system to facilitate this integration via a high-performance computing architecture such that the measurement system, the analytical modules and the computing resources can mutually adapt and steer each other. This paper describes the development of such an adaptive cyberinfrastructure system facilitated by a dynamic workflow design.

On Event Detection and Localization in Acyclic Flow Networks

Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.