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Efficient Uncertainty Analysis of TOPMODEL Using Particle Swarm Optimization

입자군집최적화 알고리듬을 이용한 효율적인 TOPMODEL의 불확실도 분석

  • Cho, Huidae (Staff Water Resources Engineer, Dewberry) ;
  • Kim, Dongkyun (Department of Civil Engineering, Hongik University) ;
  • Lee, Kanghee (Department of Civil Engineering, Hongik University)
  • 조희대 (미 듀베리사 수자원부) ;
  • 김동균 (홍익대학교 공과대학 토목공학과) ;
  • 이강희 (홍익대학교 공과대학 토목공학과)
  • Received : 2013.11.28
  • Accepted : 2014.02.07
  • Published : 2014.03.31

Abstract

We applied the ISPSO-GLUE method, which integrates the Isolated-Speciation-based Particle Swarm Optimization (ISPSO) with the Generalized Likelihood Uncertainty Estimation (GLUE) method, to the uncertainty analysis of the Topography Model (TOPMODEL) and compared its performance with that of the GLUE method. When we performed the same number of model runs for the both methods, we were able to identify the point where the performance of ISPSO-GLUE exceeded that of GLUE, after which ISPSOGLUE kept improving its performance steadily while GLUE did not. When we compared the 95% uncertainty bounds of the two methods, their general shapes and trends were very similar, but those of ISPSO-GLUE enclosed about 5.4 times more observed values than those of GLUE did. What it means is that ISPSOGLUE requires much less number of parameter samples to generate better performing uncertainty bounds. When compared to ISPSO-GLUE, GLUE overestimated uncertainty in the recession limb following the maximum peak streamflow. For this recession period, GLUE requires to find more behavioral models to reduce the uncertainty. ISPSO-GLUE can be a promising alternative to GLUE because the uncertainty bounds of the method were quantitatively superior to those of GLUE and, especially, computationally expensive hydrologic models are expected to greatly take advantage of the feature.

Acknowledgement

Supported by : 한국연구재단

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