All Issue

2018 Vol.51, Issue 9 Preview Page

Research Article

Evaluation of multi-objective PSO algorithm for SWAT auto-calibration

다목적 PSO 알고리즘을 활용한 SWAT의 자동보정 적용성 평가

Won Jin Janga
,
Yong Gwan Leea*
,
Seong Joon Kima
장 원진a
,
이 용관a*
,
김 성준a
aDepartment of Civil, Environmental and Plant Engineering, Konkuk University
a건국대학교 일반대학원 사회환경플랜트공학과
* Corresponding Author
30 September 2018. pp. 803-812
PDF XML Citation
Abstract

The purpose of this study is to develop Particle Swarm Optimization (PSO) automatic calibration algorithm with multi-objective functions by Python, and to evaluate the applicability by applying the algorithm to the Soil and Water Assessment Tool (SWAT) watershed modeling. The study area is the upstream watershed of Gongdo observation station of Anseongcheon watershed (364.8 km2) and the daily observed streamflow data from 2000 to 2015 were used. The PSO automatic algorithm calibrated SWAT streamflow by coefficient of determination (R2), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSEQ), and especially including NSEINQ (Inverse Q) for lateral, base flow calibration. The results between automatic and manual calibration showed R2 of 0.64 and 0.55, RMSE of 0.59 and 0.58, NSEQ of 0.78 and 0.75, and NSEINQ of 0.45 and 0.09, respectively. The PSO automatic calibration algorithm showed an improvement especially the streamflow recession phase and remedied the limitation of manual calibration by including new parameter (RCHRG_DP) and considering parameters range.

Keywords
Automatic calibration algorithm
Multi-objective functions
Particle swarm optimization
Python
SWAT

본 연구는 다목적함수를 고려한 입자군집최적화(Particle Swarm Optimization, PSO) 알고리즘을 Python으로 개발하고, Soil and Water Assessment Tool (SWAT) 모형에 적용하여 자동보정 알고리즘의 적용 가능성을 평가하였다. SWAT 모형의 유출 해석은 안성천의 공도 수위 관측소 상류유역(364.8 km2)을 대상으로 하였으며, 공도 지점의 2000년부터 2015년까지의 일 유량 자료를 이용하였다. PSO 자동보정은 결정계수(coefficient of determination, R2), 평균제곱근오차(RMSE), NSE 모형효율계수(Nash-Sutcliffe Efficiency, NSEQ), 특히 중간유출과 기저유출의 보정을 위해 NSEINQ (Inverse Q)를 활용하여 SWAT을 보정하였다. PSO을 통한 SWAT 모형의 자동보정과 수동보정의 유출해석 결과, 각각 R2는 0.64, 0.55, RMSE는 0.59, 0.58, NSEQ는 0.78, 0.75, NSEINQ는 0.45, 0.09의 상관성 분석결과를 보였다. PSO 자동보정 알고리즘은 수동보정에 비하여 높은 향상을 보였는데 특히 유출의 감수곡선을 개선시켰으며 적절한 매개변수 추가(RCHRG_DP)와 매개변수 범위의 설정으로 수동보정의 한계를 보완하였다.

키워드
자동보정 알고리즘
다목적함수
입자군집최적화
Python
SWAT
References
1
Arnold, J. G., Williams, J. R., Srinivasan, R., and King, K. W. (1996). SWAT manual, USDA. Agricultural Research Service and Blackland Research Center, Texas.
2
Boyle, D. P., Gupta, H. V., and Sorooshian, S. (2000). “Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods.” Water Resources Research, Vol. 36, No. 12, pp. 3663-3674.
10.1029/2000WR900207
3
Choi, H. J., Lee, S. Y., and Chae, H. S. (2013). “A testing technique for enhanced simulated annealing by branch distance neighborhood selection.” Journal of KIISE, Vol. 40, No. 6, pp. 312-321.
4
Clerc, M., and Kennedy, J. (2002). “The particle swarm explosion, stability, and convergence in a multidimensional complex space.” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, pp. 58-73.
10.1109/4235.985692
5
Duan, Q. (2003). “Global optimization for watershed model calibration.” Calibration of Watershed Models, pp. 89-104.
10.1029/WS006p0089
6
Gupta, H. V., Sorooshian, S., and Yapo, P. O. (1998). “Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information.” Water Resources Research, Vol. 34, No. 4, pp. 751-763.
10.1029/97WR03495
7
Jin, K. G., and Ha, J. S. (1997). “Genetic algorithms as optimisation tools and their applications.” Journal of the Korean Society of Marine Engineering, Vol. 21, No. 2, pp. 108-116.
8
Kamali, B., Mousavi, S. J., and Abbaspour, K. C. (2013). “Automatic calibration of HEC-HMS using single-objective and multi- objective PSO algorithms.” Hydrological Processes, Vol. 27, No. 26, pp. 4028-4042.
10.1002/hyp.9510
9
Kennedy, J., and Eberhart, R. (1995). “PSO optimization.” Proceedings IEEE International Conference on Neural Networks, Vol. 4, pp. 1941-1948.
10
Kim, D. R., and Kim, S. J. (2017). “A study on parameter estimation for SWAT calibration considering streamflow of long-term drought periods.” Journal of the Korean Society of Agricultural Engineers, Vol. 59, No. 2, pp. 19-27.
10.5389/KSAE.2017.59.2.019
11
Kim, H. J., Chun, H. H., and An, N. H. (2008). “Hull form optimization using parametric modification functions and global optimization.” Journal of the Society of Naval Architects of Korea, Vol. 45, No. 6, pp. 590-600.
10.3744/SNAK.2008.45.6.590
12
Kim, T. S., Jeong, I. W., Koo, B. Y., and Bae, D. H. (2007). “Optimization of tank model parameters using multi-objective genetic algorithm (I): Methodology and model formulation.” Journal of Korea Water Resources Association, Vol. 42, No. 9, pp. 677-685.
10.3741/JKWRA.2007.40.9.677
13
Kwon, Y. S., Bae, M. J., Hwang, S. J., and Park, Y. S. (2008). “Application of particle swarm optimization (PSO) for prediction of water quality in agricultural reservoirs of Korea.” Korean Journal of Limnology, Vol. 41, pp. 11-20.
14
Madsen, H. (2000). “Automatic calibration of a conceptual rainfall- runoff model using multiple objectives.” Journal of Hydrology, Vol. 235, No. 3-4, pp. 276-288.
10.1016/S0022-1694(00)00279-1
15
Mkhwanazi, M., Chávez, J. L., and Rambikur, E. H. (2012). “Comparison of large aperture scintillometer and satellite- based energy balance models in sensible heat flux and crop evapotranspiration determination.” International Journal of Remote Sensing Applications, Vol. 2, No. 1, pp. 24-30.
16
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R. (2001). Soil and water assessment tool: theoretical documentation. Texas Water Resources Institute.
17
Nelder, J. A., and Mead, R. A. (1965). “Simplex method for function minimization.” Computer Journal, Vol. 7, No. 4, pp. 308-313.
10.1093/comjnl/7.4.308
18
Park, B. J., Oh, S. K., Kim, Y. S., and An, T. C. (2006). “Comparative study on dimensionality and characteristic of PSO.” International Journal of Control, Automation, and Systems, Vol. 12, No. 4, pp. 328-338.
19
Pushpalatha, R., Perrin, C., Le Moine, N., and Andréassian, V. (2012). “A review of efficiency criteria suitable for evaluating low-flow simulations.” Journal of Hydrology, Vol. 420-421, pp. 171-182.
10.1016/j.jhydrol.2011.11.055
20
Tolson, B. A., and Shoemaker, C. A. (2007). “Dynamically dimensioned search algorithm for computationally efficient watershed model calibration.” Water Resources Research, Vol. 43, No. 1.
10.1029/2005WR004723
21
Yapo, P. O., Gupta, H. V., and Sorooshian, S. (1998). “Multi- objective global optimization for hydrologic models.” Journal of Hydrology, Vol. 204, No. 1-4, pp. 83-97.
10.1016/S0022-1694(97)00107-8
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 51
  • No :9
  • Pages :803-812
  • Received Date : 2018-07-06
  • Revised Date : 2018-08-01
  • Accepted Date : 2018-08-01
  • DOI :https://doi.org/10.3741/JKWRA.2018.51.9.803
Journal Informaiton Journal of Korea Water Resources Association Journal of Korea Water Resources Association
Online Submission
Archives
  • scopus
  • KCI-수자원
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close