9733788 Ranjithan The objectives of this research are to 1) investigate ways to enhance the capabilities of genetic algorithms (GAs) for complex environmental systems analysis, 2) develop and integrate into existing courses an interactive training module to assist in teaching the fundamentals of GAs and their potential uses in environmental systems analysis, 3) explore applications of the methodology in watershed management through a series of realistic case-studies and 4) integrate the case-studies and their findings in courses related to environmental systems analysis and watershed management. Environmental management problems are inherently complex because the governing physical, chemical and biological processes and their interactions are nonlinear and difficult to represent in convenient mathematical equations, further complicated by the multitude of other important issues that must be considered, such as economics, environmental impact and reliability. A major component of this work is optimization for generation and identification of efficient alternative solutions. Traditional optimization procedures impose severe restrictions in using complex nonlinear relationships common to environmental problem solving. Genetic algorithms have been shown to be powerful in addressing this issue and have the potential to make significant improvement to the state-of-the-art. New operators and procedures will be developed with the goal of extending the use of GAs to present a limited set of alternative solutions to complex problems, each of which satisfy all quantified constraints, but which are otherwise as far apart as possible in the decision space. In this way a manageable number of alternatives can be evaluated that constitute a wide assortment of choices on the less quantified parameters. The academic plans include the development of teaching modules and interactive techniques for both students and practitioners; training aids will permit students to learn to use the tool and to compare its results with those of other adaptive optimization techniques. To ensure that the proposed systems methodologies are applicable in practice, an array of realistic applications associated with watershed management has been identified and will be investigated in collaboration with practitioners and other researchers. An extensive case study of the Neuse River Basin in North Carolina will be included. ***
