Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation
AbstractGlobal optimization methods linked with simulation models are widely used for automated calibration and serve as useful tools for searching for cost-effective alternatives for environmental management. A genetic algorithm (GA) and shuffled complex evolution (SCE-UA) algorithm were linked with the Long-Term Hydrologic Impact Assessment (L-THIA) model, which employs the curve number (SCS-CN) method. The performance of the two optimization methods was compared by automatically calibrating L-THIA for monthly runoff from 10 watersheds in Indiana. The selected watershed areas ranged from 32.7 to 5844.1 km2. The SCS-CN values and total five-day rainfall for adjustment were optimized, and the objective function used was the Nash-Sutcliffe value (NS value). The GA method rapidly reached the optimal space until the 10th generating population (generation), and after the 10th generation solutions increased dispersion around the optimal space, called a cross hair pattern, because of mutation rate increase. The number of looping executions influenced the performance of model calibration for the SCE-UA and GA method. The GA method performed better for the case of fewer loop executions than the SCE-UA method. For most watersheds, calibration performance using GA was better than for SCE-UA until the 50th generation when the number of model loop executions was around 5150 (one generation has 100 individuals). However, after the 50th generation of the GA method, the SCE-UA method performed better for calibrating monthly runoff compared to the GA method. Optimized SCS-CN values for primary land use types were nearly the same for the two methods, but those for minor land use types and total five-day rainfall for AMC adjustment were somewhat different because those parameters did not significantly influence calculation of the objective function. The GA method is recommended for cases when model simulation takes a long time and the model user does not have sufficient time for an optimization program to search for the best values of calibration parameters. For other cases, the SCE-UA program is recommended for automatic model calibration. View Full-Text
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Jeon, J.-H.; Park, C.-G.; Engel, B.A. Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation. Water 2014, 6, 3433-3456.
Jeon J-H, Park C-G, Engel BA. Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation. Water. 2014; 6(11):3433-3456.Chicago/Turabian Style
Jeon, Ji-Hong; Park, Chan-Gi; Engel, Bernard A. 2014. "Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation." Water 6, no. 11: 3433-3456.