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Parameter Calibrations of a Daily Rainfall-Runoff Model Using Global Optimization Methods

전역최적화 기법을 이용한 강우-유출모형의 매개변수 자동보정

  • Published : 2002.10.01

Abstract

Two global optimization methods, the SCE-UA method and the Annealing-Simplex(A-S) method for calibrating a daily rainfall-runoff model, a Tank model, was compared with that of the Downhill Simplex method. In synthetic data study, 100% success rates for all objective functions were obtained from the A-S method, and the SCE-UA method was also consistently able to obtain good estimates. The Downhill Simplex method was converged to the true values only when the initial guess was close to the true values. In the historical data study, the A-S method and the SCE-UA method showed consistently good results regardless of objective function. An objective function was developed, which puts more weight on the low flows.

본 연구에서는 전체탐색기법 중 Simplex법의 원리를 이용한 SCE-UA법과 Annealing-Simplex (A-S)법을 일유출량 추정 수문모형인 탱크모형의 매개변수 보정에 적용하여 국부탐색기법인 Downhill Simplex법의 결과와 비교하여 탐색능력을 평가하였다. 오차가 없는 합성자료를 사용한 보정에서 A-S법이 목적함수에 관계없이 전역최적해를 탐색하는 결과를 나타냈으며, SCE-UA법은 ABSERR를 목적함수로 사용할 경우에 전역최적해를 탐색하는 결과를 나타냈으며, 다른 목적함수를 사용하는 경우에는 최적해에 가까운 탐색결과를 나타냈다. Downhill Simplex법은 초기값에 따라 다근 탐색결과를 나타냈으며, 최적해에 가까운 초기값을 사용할 경우 전역 최적해를 탐색하는 결과를 나타냈다. 실측자료를 사용한 보정에서는 A-S법과 SCE-UA법이 목적함수에 관계없이 양호한 결과를 나타냈다. 두 개의 서로 다른 단일 목적함수를 조합하여 만든 목적함수 중 DRMS와 NS의 조합에 의해 만들어진 목적함수인 DN이 다른 목적함수보다 저유량에 비중을 더 둔 예측결과를 나타냈으며, 전체 자료기간에 대해서 양호한 예측결과론 나타냈다.

Keywords

References

  1. 박봉진, 차형선, 김주환(1997). '유전자 알고리즘을 이용한 저류함수모형의 매개변수 추정에 관한 연구', 한국수자원학회 논문집, 한국수자원학회, pp. 347-355
  2. 신성철, 강경석, 서병하(2001). 'Tank Model의 매개변수 최적화에 관한 연구', 한국수자원학회 학술발표회 논문집, 한국수자원학회, pp. 158-163
  3. 심순보, 김선구, 고석구, (1992). '최적화 기법에 의한 저류함수 유출모형의 자동보정', 대한토목학회지, 대한토목학회, Vol. 12, No. 3, pp. 127-137
  4. 이길성, 김상욱(2001). '유전자 알고리즘을 사용한 SSARR 모형의 자동보정', 대한토목학회지, 대한토목학회, Vol. 21, No. 3-B, pp. 171-183
  5. Cardso, M. F., Salcedo R. L., and Azevedo S. F. (1996). 'The simplex-simulated annealing approach to continuous nonlinear optimization.' Computers and Chemical Engineering, 20(9), 1065-1080 https://doi.org/10.1016/0098-1354(95)00221-9
  6. Cooper V. A., Nguyen V. T. V., and Nicell J. A. (1997). 'Evaluation of global optimization methods for conceptual rainfall-runoff model calibration', Water Science and Technology, 36(5), 53-60 https://doi.org/10.1016/S0273-1223(97)00461-7
  7. Duan Q., Soroooshian S., and Gupta V. K. (1994). 'Optimal use of the SCE-UA global optimization method for calibrating watershed models', Journal of Hydrology, 158, 265-284 https://doi.org/10.1016/0022-1694(94)90057-4
  8. Duan Q., Soroooshian S., and Gupta V. K. (1992). 'Effective and efficient global optimization for conceptual rainfall-runoff models.' Water Resources Research, 28(4), 1015-1031 https://doi.org/10.1029/91WR02985
  9. Freedman V. L., Lopes, V. L., and Hernandez. M. (1998). 'Parameter identifiability for catchment-scale erosion modelling: a comparison of optimization algorithms,' Journal of Hydrology, 207, 83-97 https://doi.org/10.1016/S0022-1694(98)00131-0
  10. Gan T. Y. and Biftu G. F. (1996). 'Automatic calibration of conceptual rainfall-runoff models: Optimization algorithms, catchment conditions, and model structure.' Water Resources Research, 32(12), 3513-3524 https://doi.org/10.1029/96WR02195
  11. Gan T. Y. Dlamin E. M., and Biftu G. F. (1997). 'Effects of model complexity and structure, data quality, and objective functions on hydrologic modeling.' Journal of Hydrology, 192, 81-103 https://doi.org/10.1016/S0022-1694(96)03114-9
  12. Gupta H. V., Sorooshian S., and Yapo P. O. (1999). 'Status of automatic calibration for hydrologic model : comparison with multilevel expert calibration.' Journal of Hydrologic Engineering, 4(2), 135-143 https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135)
  13. Isabel D. and Villeeneuve J. P. (1986). 'Importance of the convergence criterion in the automatic calibration of hydrologic models.'Water Resources Research, 22(10), 1367-1370 https://doi.org/10.1029/WR022i010p01367
  14. Kvasnickas V. and Pospichal J.(1997). 'A hybrid of simplexmethod simulated annealing.' Chemometric and Intelligent Laboratory System, 39, 161-173 https://doi.org/10.1016/S0169-7439(97)00071-3
  15. Lee Y. H. and Singh V. P. (1999). 'Tank model using Kalman filter.' Journal of Hydrologic Engineering, 4(4), 344-349 https://doi.org/10.1061/(ASCE)1084-0699(1999)4:4(344)
  16. Liong S. Y., Khu S. T., and Chan W. T. (2001). 'Derivation of Pareto front with genetic algorithm and neural network.' Journal of Hydrologic Engineering, 6(1), 52-61 https://doi.org/10.1061/(ASCE)1084-0699(2001)6:1(52)
  17. Nelder J. A and Mead R.(1965). 'A simplex method for function minimization.' Computer Journal, 7(4), 308-313 https://doi.org/10.1093/comjnl/7.4.308
  18. Pan L. and Wu L. (1998). 'A hybrid global optimization method for inverse estimation of hydraulic parameters : Annealing-simplex method.' Water Resources Research, 34(9), 2261-2269 https://doi.org/10.1029/98WR01672
  19. Press, W. H., Teukolsky S. A., Vetterling W. T., and Flannery B. P. (1992). Numerical Recipes in C, 2nd edtion, Cambridge University Press, Cambridge, U. K.
  20. Sorooshian, S., and Gupta V. K. (1983). 'Automatic calibration of conceptual rainfallrunoff models : the question of parameter observability and uniqueness.' Water Resources Research, 19(1), 260-268 https://doi.org/10.1029/WR019i001p00260
  21. Thyer M., Kuczera G., and Bates B. C. (1999). 'Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulation annealing algorithms.' Water Resources Research, 35(3), 767-773 https://doi.org/10.1029/1998WR900058
  22. World Meteological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. Operational Hydrology Report No.7, Geneva, Switzerland
  23. Yapo P. O., Gupta H. V., and Sorooshian S. (1996). 'Automatic calibration of conceptual rainfall-runoff models : sensitivity to calibration data.' Journal of Hydrology, 181, 23-48 https://doi.org/10.1016/0022-1694(95)02918-4

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