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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, including a call to address larger-scale problems that are wrought with uncertainty and an expanded need for cross fertilization and collaboration among our field's subdisciplines. Evolutionary computation will continue to evolve in the future as we encounter increased problem complexities and uncertainty and as the societal pressure for more innovative and efficient solutions rises. DOI: 10.1061/(ASCE)WR.1943-5452.0000053 CE Database subject headings: Algorithms; Computation; Water resources; Water management; Planning. Author keywords: Evolutionary algorithm; Evolutionary computation; Genetic algorithm.

An integrative computational methodology is developed for the management of nonpoint source pollution from watersheds. The associated decision support system is based on an interface between evolutionary algorithms (EAs) and a comprehensive watershed simulation model, and is capable of identifying optimal or near-optimal land use patterns to satisfy objectives. Specifically, a genetic algorithm (GA) is linked with the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT) for single objective evaluations, and a Strength Pareto Evolutionary Algorithm has been integrated with SWAT for multiobjective optimization. The model can be operated at a small spatial scale, such as a farm field, or on a larger watershed scale. A secondary model that also uses a GA is developed for calibration of the simulation model. Sensitivity analysis and parameterization are carried out in a preliminary step to identify model parameters that need to be calibrated. Application to a demonstration watershed located in Southern Illinois reveals the capability of the model in achieving its intended goals. However, the model is found to be computationally demanding as a direct consequence of repeated SWAT simulations during the search for favorable solutions. An artificial neural network (ANN) has been developed to mimic SWAT outputs and ultimately replace it during the search process. Replacement of SWAT by the ANN results in an 84% reduction in computational time required to identify final land use patterns. The ANN model is trained using a hybrid of evolutionary programming (EP) and the back propagation (BP) algorithms. The hybrid algorithm was found to be more effective and efficient than either EP or BP alone. Overall, this study demonstrates the powerful and multifaceted role that EAs and artificial intelligence techniques could play in solving the complex and realistic problems of environmental and water resources systems. CE Database subject headings: Algorithms; Neural networks; Watershed management: Pollution control: Calibration: Computation.

An optimal control methodology is developed to evaluate reservoir management policies that minimize sediment scour and deposition in multiple-reservoir river networks. Consideration is given to adverse effects occurring in both reservoirs and rivers of a hydraulic network. The sedimentation problem is formulated as a discrete-time optimal control problem in which a successive approximation linear quadratic regulator optimization scheme is coupled with the U.S. Army Corps of Engineers HEC-6 sediment transport simulation model. Operational constraints imposed on reservoir storage levels and releases are accommodated using either a bracket or hyperbolic penalty function method. The resulting model also allows the user to evaluate policies that alternatively maximize sedimentation or consider adverse effects only at specified locations. Comparisons of the computational efficiencies between the successive approximation linear quadratic regulator and differential dynamic programming methodology and between different penalty functions are performed. Capabilities of the model are demonstrated through applications to a hypothetical three-reservoir network and the Yazoo Basin river-reservoir network in Mississippi.