SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Decision support for watershed management using evolutionary algorithms 
Authors:Muleta, M.K. and J.W. Nicklow 
Journal:Journal of Water Resources Planning and Management 
Volume (Issue):131(1) 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:hydrologic and pollutant 
Primary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Secondary Application Category:sediment loss and transport 
Watershed Description:133 km^2 Big Creek, a tributary of the Lower Cache River located in southern Illinois, U.S. 
Calibration Summary:Daily (1999-2001; Ilinois Water Survey "water years"): flow r2 = .69 sediment r2 = .42 
Validation Summary:verification was not performed due to a lack of data 
General Comments:An automatic calibration using a built-in genetic algorithm (GA) was performed with 42 Parameters, including 27 for a "representative HRU." A stepwise regression was used for a sensitivity analysis was performed on the 42 parameters (to aid in the calibration process), that was performed using latin Hypercube sampling that generated 300 input samples. The GA was used to evaluate the best choice of practices for reducing sediment. The ability of an Artificial Neural Network (ANN) to mimic SWAT is also reported. 
Abstract: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.