Evolutionary Algorithms to Aid Watershed Management


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 heuristic search procedures to identify good management strategies that meet the problem-specific (e. g., fiscal or environmental) objectives and constraints. The design of the software framework is described with a demonstration of its capabilities via a case study involving several scenarios of a watershed management problem.