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ARS Home » Pacific West Area » Corvallis, Oregon » Forage Seed and Cereal Research Unit » Research » Publications at this Location » Publication #206808
Title: Multi-objective Automatic Calibration of a Semi-distributed Watershed Model Using Pareto Ordering Optimization and Genetic Algorithm

Author
item CONFESOR, REMEGIO - OREGON STATE UNIVERSITY
item Whittaker, Gerald

Submitted to: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)
Publication Type: Proceedings
Publication Acceptance Date: 6/30/2006
Publication Date: 8/31/2006
Citation: Confesor, R.B., Whittaker, G.W. 2006. Multi-objective Automatic Calibration of a Semi-distributed Watershed Model Using Pareto Ordering Optimization and Genetic Algorithm. Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE). V 6 Paper Number 062128.

Interpretive Summary: Standard practice in hydrologic and environmental modeling requires that the model be calibrated to fit the observed data. Calibration of a complex model is a demanding and time consuming task. The task is made even more difficult by the requirement that the model be calibrated to fit more than one objective. For example, it is very useful to have a hydrologic model calibrated to rainfall driven stream flow as well as the base stream flow. Automatic calibration refers to a computational method where the computer replaces the human in the calibration process. The object of this study was to develop a method of automatic calibration that used multiple objectives and could calibrate a hydrologic model using a large number of variables. Using the method developed here, we were able successfully calibrate a hydrologic model using a number of variables and multiple objectives that were far beyond the capacity of a human expert.

Technical Abstract: This study explored the application of a multi-objective evolutionary algorithm (MOEA) and Pareto ordering in the multiple-objective automatic calibration of the Soil and Water Assessment Tool (SWAT). SWAT was calibrated in the Calapooia watershed, Oregon, USA, with two different pairs of objective functions in a cluster of 24 parallel computers. The non-dominated sorting genetic algorithm (NSGA-II), a fast MOEA, and SWAT were called from a parallel genetic algorithm library (PGAPACK) to determine the Pareto optimal set. One hundred fifty-five parameters were explicitly calibrated (9 for each 17 hydrologic response units [HRUs] and 2 for the whole watershed). With the root mean square error (RMSE) and mean absolute error (MAE) of the daily flows as objective functions, the Pareto front converged to a narrow range of solution set. A wider Pareto optimal front was formed when the RMSE of high and low flows was used as objective functions. The calibrated SWAT model simulated well the daily streamflow of the Calapooia River for a 3-year period. The daily Nash-Sutcliffe efficiency was 0.85 at calibration and 0.80 at validation. Automatic multi-objective calibration of a complex process-based watershed model such as SWAT was successfully implemented using Pareto ordering optimization and an MOEA. Simultaneous automatic-calibration of flows and water quality parameters for the whole watershed and for different sub-basins, dynamic link with economic models, and integration of uncertainty and sensitivity methods are now explored.