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Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
The watershed management approach is a framework for addressing water quality problems at a watershed scale in an integrated manner that considers many conflicting issues including cost, environmental impact and equity in evaluating alternative control strategies. This framework enhances the capabilities of current environmental analysis frameworks by the inclusion of additional systems analytic tools such as optimization algorithms that enable efficient search for cost effective control strategies and uncertainty analysis procedures that estimate the reliability in achieving water quality targets. Traditional optimization procedures impose severe restrictions in using complex nonlinear environmental processes within a systematic search. Hence, genetic algorithms (GAs), a class of general, probabilistic, heuristic, global, search procedures, are used. Current implementation of this framework is coupled with US EPA's BASINS software system. A component of the current research is also the development of GA object classes and optimization model classes for generic use. A graphical user interface allows users to formulate mathematical programming problems and solve them using GA methodology. This set of GA object and the user interface classes together comprise the Generic Genetic Algorithm Based Optimizer (GeGAOpt), which is demonstrated through applications in solving interactively several unconstrained as well as constrained function optimization problems. Design of these systems is based on object oriented paradigm and current software engineering practices such as object oriented analysis (OOA) and object oriented design (OOD). The development follows the waterfall model for software development. The Unified Modeling Language (UML) is used for the design. The implementation is carried out using the JavaTM programming environment.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
The watershed management approach is a framework for addressing water quality problems at a watershed scale in an integrated manner that considers many conflicting issues including cost, environmental impact and equity in evaluating alternative control strategies. This framework enhances the capabilities of current environmental analysis frameworks by the inclusion of additional systems analytic tools such as optimization algorithms that enable efficient search for cost effective control strategies and uncertainty analysis procedures that estimate the reliability in achieving water quality targets. Traditional optimization procedures impose severe restrictions in using complex nonlinear environmental processes within a systematic search. Hence, genetic algorithms (GAs), a class of general, probabilistic, heuristic, global, search procedures, are used. Current implementation of this framework is coupled with US EPA's BASINS software system. A component of the current research is also the development of GA object classes and optimization model classes for generic use. A graphical user interface allows users to formulate mathematical programming problems and solve them using GA methodology. This set of GA object and the user interface classes together comprise the Generic Genetic Algorithm Based Optimizer (GeGAOpt), which is demonstrated through applications in solving interactively several unconstrained as well as constrained function optimization problems. Design of these systems is based on object oriented paradigm and current software engineering practices such as object oriented analysis (OOA) and object oriented design (OOD). The development follows the waterfall model for software development. The Unified Modeling Language (UML) is used for the design. The implementation is carried out using the JavaTM programming environment.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
Watershed management requires consideration of a multitude of factors affecting water quality at the watershed-scale while integrating point and non-point sources of pollution and control. While the existing water quality modeling systems and associated quantitative tools can assist in some aspects of Total Maximum Daily Load (TMDL) development for a watershed, their abilities to assist in determining efficient management strategies are limited. Typically, the best a user can do is employ these tools manually to explore the solution space via a trial-and-error process, which is inefficient for finding management strategies that consider water quality as well as a multitude of other design issues simultaneously. Recent implementation of the STAR (STrategy, Analysis, and Reporting) system incorporates a set of systems analytic tools to assist decisions-makers explore and identify alternative management strategies. The main engine of the STAR system is a genetic algorithm-based optimization technique, which is coupled with additional tools such as an uncertainty propagation tool, a solution reporting system, and an incremental strategy development system to form a decision support framework. This paper describes some of the capabilities of this framework through several illustrative scenarios for the Yellow River watershed in Gwinnett County, Georgia, which conducted a comprehensive, countywide TMDL investigation to assess the current water quality conditions. The STAR system's capabilities are employed to identify ways to achieve minimum total phosphorous (TP) levels via point and nonpoint source controls, as well as characterize the implications of future urban development on TP levels. Noninferior tradeoffs between urban development and TP levels at different degrees of point source controls are generated. The range of uses of the STAR system in considering the integrated effect of point and non-point sources in watershed management is demonstrated throughout these.
Author: Adriana Debora Piemonti Publisher: ISBN: Category : Genetic algorithms Languages : en Pages : 197
Book Description
Degradation of watersheds is a major concern in areas where adverse climate effects and unsustainable use of the natural resources have caused extensive stresses to watershed systems (e.g., increased floods, increased droughts, worsened in-stream water quality) through the years. While considerable efforts are being made to generate technical solutions that focus on plans of spatially-distributed conservation practices (e.g., Wetlands, Filter Strips, Grassed Waterways, Crop Management practices, etc.) for restoration of existing conditions in the watersheds, adoption and implementation of these solutions require a better understanding of constraints faced by affected stakeholders and decision makers. Participatory modeling and design approaches have, as a result, become popular in the recent past to support a community's engagement during the modeling process and during development of potential scenarios of plans (or, design alternatives). And now, with new and ongoing developments in Web 2.0 technologies, there is an even greater need for research that examines how large number of stakeholders can be engaged in the development of design alternatives via the internet-based, decision support environments. The overarching goal of this research is to investigate how stakeholder participation ("humans") and Interactive Genetic Algorithms ("computer") can be coupled in a web-based watershed decision support system (DSS) called WRESTORE (Watershed REstoration using Spatio Temporal Optimization of REsources- http://wrestore.iupui.edu/), in order to generate user-preferred design alternatives of distributed conservation practices on a watershed landscape. An important component of this goal is to also improve the understanding of how human behavior on the graphical user interface (GUI) of the DSS can be observed and evaluated in real-time, and then learned from to further improve the performance of the underlying search algorithm. Four specific objectives were addressed in this work to accomplish the overall goal: · Objective 1: Observe interactions of multiple users with the GUI of a web-based watershed DSS (WRESTORE, http://wrestore.iupui.edu/) during interactive search experiments, and then use Usability metrics (response times, clicking events and confidence levels) to evaluate the differences and similarities in user behaviors and interactions. · Objective 2: Examine relationships between the type of users (e.g., stakeholders versus surrogates), the Usability metrics, and patterns in the watershed-scale plans of conservation practices generated by the multi-objective Interactive Genetic Algorithm embedded in WRESTORE. · Objective 3: Examine relationships between the type of users, the Usability metrics, and patterns in the user-preferred, sub-basin-scale plans of conservation practices generated by the multi-objective Interactive Genetic Algorithm embedded in WRESTORE. · Objective 4: Develop and test novel human-guided search operators that adaptively learn for patterns in user-preferred alternatives generated by the multi-objective Interactive Genetic Algorithm, and, as a result, improve the convergence rate of the search algorithm for generating design alternatives that conserve these learned patterns. Results show that there is a clear difference on how different types of users interact with the Interactive Optimization system. The observed relationship between confidence levels, time spent on a task, and number of mouse clicking events, indicated that participants who were able to use the WRESTORE GUI to gather more information and had a higher rate of time per number of clicks, tended to increase their levels of self-confidence in their own feedback. Also, when engaging with watershed stakeholders versus non-stakeholders (or, surrogates), 67% of the stakeholder participants steadily increased their average self-confidence levels as they continued to interact with the tool, in contrast to only 29% of surrogate participants who also showed an increase in their self-confidence levels through time. Such usability and confidence level evaluations provide assessments on which participant was potentially generating reliable feedback data for the search algorithm to use. An analysis of design alternatives generated by the individuals in both stakeholder and non-stakeholder groups showed that a majority (67%) of the stakeholder participants found a higher percentage (on and average 52%) of preferred design alternatives via the interactive search process. Also, users who were focused on assessing the suitability of design alternatives for the entire watershed trended to demonstrate a bias for one of the watershed-scale objective functions. In contrast, users, who were focused on assessing the suitability of design alternatives at only a few local sub-basins in the watershed, did not demonstrate any clear bias for any one of the watershed-scale objective functions. Additionally, patterns were observed in the design of decision alternatives generated by the human-centered search process, which further divulged potential user preferences related to the decision space for example, whether a specific participant preferred a certain practice over another, or a certain location over another for a specific practice. Finally, to improve the convergence rates of the Interactive Genetic Algorithm in WRESTORE, we investigated whether observed patterns in decisions (especially, when users were focused on local sub-regions of the watershed) can be used to improve the search for user-desire designs. A novel Interactive Genetic Algorithm with adaptive, human-guided, selection, crossover and mutation operators was proposed. The new algorithm was tested with six types of simulated participants (three deterministic and three probabilistic users) developed from the feedback data of three real participants. Results of search experiments with the novel adaptive IGA operators indicated a faster convergence than the default IGA, for two out of three deterministic simulated users. However, none of the probabilistic user showed a convergence different than the default values. This indicates that while current results indicate promise, there is need for additional research on adaptive, human-guided IGA operators, especially when noisy/stochastic users participate in the search. Additionally, adaptation of search operators have the potential to improve convergence rates when participatory design is done via Interactive Genetic Algorithms.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
Watershed management is a complex process involving multiple uses, diverse stakeholders, and a variety of computer-based hydrologic and hydraulic simulation models. Exploring for efficient solutions and making decisions about the best integrated management strategies to implement can be improved through the use of quantitative systems analytic techniques. In addition to identifying mathematically optimal solutions, these techniques should also be able to consider issues that may not be properly represented in the models or may be in conflict with one another. As the complexities of the system models grow, contemporary heuristic search methods, including evolutionary algorithms (EAs), are becoming increasingly common in quantitative analysis of such challenging decision-making problems. More research is needed to enhance and extend the capabilities of these newer search methods to meet the growing challenges. Further, these new systems analytic capabilities are best made accessible to practitioners through a generic computational framework that integrates the system simulation models with the suite of search techniques. Therefore, the purpose of this research is to develop new EA-based system analytic methods for addressing integrated watershed management problems and a computational framework within which their capabilities are enabled for watershed management applications. EA-based methods to generate good alternative solutions and for multiobjective optimization have been developed and tested, and their performances compare well with those of other procedures. These new methods were also demonstrated through successful applications to realistic problems in watershed management. These techniques were integrated into and implemented within a new computer-based decision support framework that supports the integration of the user's preferred watershed models, methods to perform uncertainty and/or sensitivity analyses thereon, and multiple state-of-the-art optimization.
Author: Roger M. Miranda Publisher: ISBN: Category : Languages : en Pages : 1158
Book Description
This dissertation describes the development of a decision support tool designed to facilitate and enhance collaborative binational decision making associated with integrated transboundary water quality planning and management in the Lower Rio Grande/Río Bravo. The Lower Rio Grande Water Quality Initiative Decision Support System (LRGWQIDSS) is the result of a multidisciplinary effort to integrate the results of qualitative social science research and traditional and novel engineering and geographic information systems (GIS) methods associated with the modeling, analysis, and visualization of watershed, water quality and natural resources data. The LRGWQIDSS incorporates information currently used by urban planning and natural resource management organizations working along the Texas-Mexico border area and provides a means to analyze and display the information in a way that is useful to institutional and noninstitutional actors involved in transboundary water quality planning efforts. The analysis of the institutional arrangements currently in place to protect water quality in the Lower Rio Grande/Río Bravo played an important role in the design and development of the LRGWQIDSS and its successful application. The tool’s development represents a case study in the importance of the role of institutional analysis in the successful development of decision support systems for transboundary water quality management
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Author: Amey Vijay Parandekar
Author: Can Ali Kuterdem
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Author: Adriana Debora Piemonti
Author: Dreux J. Watermolen
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Author: American Water Works Association
Author: Roger M. Miranda
Author: John W. Labadie