Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation

Chan-Gi Park,Bernard Engel,Ji-Hong Jeon

doi:10.3390/w6113433

Chan-Gi Park, Bernard Engel + Show 1 more

Open Access

https://doi.org/10.3390/w6113433

Copy DOI

Abstract

Global 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.

Highlights

Optimization techniques have become widely used in computer modeling applications.A major advantage of the application of optimization techniques with models is to obtain better results without significant effort and time
Optimization techniques can be divided into two groups: global search methods such as Genetic Algorithm (GA) [1] and Shuffled Complex Evolution (SCE-UA) [2]
The number of model loop executions by the optimization program influenced which global optimization program between GA and SCE-UA was better for automatic calibration of SCS-curve number (CN) model runoff in this study

Summary

Introduction

Optimization techniques have become widely used in computer modeling applications. A major advantage of the application of optimization techniques with models is to obtain better results without significant effort and time. Optimization techniques can be divided into two groups: global search methods such as Genetic Algorithm (GA) [1] and Shuffled Complex Evolution (SCE-UA) [2]. Algorithm, and local search methods, such as the downhill simplex method [3], the pattern search method [4], and the rotating direction method [5]. Numerous studies have reported that global search methods performed better than local search methods [6,7,8,9,10]. Numerous studies reported that the SCE-UA algorithm provided better estimates of the solution compared with other global and local search procedures for calibrating watershed models. Cooper et al [6]

Methods

Results

Discussion

Conclusion