Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Analysis of the applicability of parameter estimation methods for a transient storage model

저장대모형의 매개변수 산정을 위한 최적화 기법의 적합성 분석

  • Noh, Hyoseob (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Baek, Donghae (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Seo, Il Won (Department of Civil and Environmental Engineering, Seoul National University)
  • 노효섭 (서울대학교 건설환경공학부) ;
  • 백동해 (서울대학교 건설환경공학부) ;
  • 서일원 (서울대학교 건설환경공학부)
  • Received : 2019.07.16
  • Accepted : 2019.09.20
  • Published : 2019.10.31
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Abstract

A Transient Storage Model (TSM) is one of the most widely used model accounting for complex solute transport in natural river to understanding natural river properties with four TSM key parameters. The TSM parameters are estimated via inverse modeling. Parameter estimation of the TSM is carried out by solving optimization problem about finding best fitted simulation curve with measured curve obtained from tracer test. Several studies have reported uncertainty in parameter estimation from non-convexity of the problem. In this study, we assessed best combination of optimization method and objective function for TSM parameter estimation using Cheong-mi Creek tracer test data. In order to find best optimization setting guaranteeing convergence and speed, Evolutionary Algorithm (EA) based global optimization methods, such as CCE of SCE-UA and MCCE of SP-UCI, and error based objective functions were compared, using Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL). Overall results showed that multi-EA SC-SAHEL with Percent Mean Squared Error (PMSE) objective function is the best optimization setting which is fastest and stable method in convergence.

Transient Stroage Model (TSM)은 하천을 본류대와 저장대로 나누어 각각에 대한 오염물의 혼합거동을 해석함으로써 복잡한 하천에 유입된 오염물질 혼합을 이해하는 데에 가장 많이 이용되는 모형 중 하나이다. TSM의 매개변수들은 역산모형을 통해 산정하게 되는데 이는 자연하천에서 추적자실험을 통해 계측된 농도곡선에 가장 잘 맞는 TSM 모의 농도곡선을 찾는 최적화 문제이다. 저장대모형의 매개변수 산정에 관한 선행 연구들에 의해 매개변수를 산정하는 최적화 문제의 비볼록(non-convex) 특성에서 오는 불확실성이 보고되어 왔다. 본 연구에서는 청미천에서 수행된 추적자실험으로부터 취득된 농도곡선을 이용해 최상의 최적화 기법과 목적함수의 조합에 대해 분석하였다. 최적화 문제의 수렴성과 수렴 속도를 모두 만족하는 최적화 조건을 결정하기 위해 SCE-UA의 CCE와 SP-UCI의 MCCE와 같은 진화 알고리즘 기반의 전역 최적화 방법들과 오차 기반 목적함수들을 Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL)을 활용해 비교하였다. 전반적인 변수 산정 결과 여러 EA를 동시에 적용한 SC-SAHEL을 평균 제곱오차를 목적함수로 한 방법이 가장 빠르고 가장 안정적으로 최적해에 수렴하는 것으로 나타났다.

Keywords

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