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Application of MCMC–GSA model calibration method to urban runoff quality modeling

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  • Kanso, A.
  • Chebbo, G.
  • Tassin, B.

Abstract

In stormwater quality modeling, estimating the confidence level in conceptual model parameters is necessary but difficult. The applicability and the effectiveness of a method for model calibration and model uncertainty analysis in the case of a four parameters lumped urban runoff quality model are illustrated in this paper. This method consists of a combination of the Metropolis algorithm for parameters’ uncertainties and correlation assessment and a variance-based method for global sensitivity analysis. The use of the Metropolis algorithm to estimate the posterior distribution of parameters through a likelihood measure allows the replicated Latin hypercube sampling method to compute the parameters’ importance measures. Calibration results illustrate the usefulness of the Metropolis algorithm in the assessment of parameters’ uncertainties and their interaction structure. The sensitivity analysis demonstrates the insignificance of some parameters in terms of driving the model to have a good conformity with the data. This method provides a realistic evaluation of the conceptual description of the processes used in models and a progress in our capability to assess parameters’ uncertainties.

Suggested Citation

  • Kanso, A. & Chebbo, G. & Tassin, B., 2006. "Application of MCMC–GSA model calibration method to urban runoff quality modeling," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1398-1405.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1398-1405
    DOI: 10.1016/j.ress.2005.11.051
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    Cited by:

    1. Boulange, Julien & Watanabe, Hirozumi & Akai, Shinpei, 2017. "A Markov Chain Monte Carlo technique for parameter estimation and inference in pesticide fate and transport modeling," Ecological Modelling, Elsevier, vol. 360(C), pages 270-278.
    2. Yuan, Jun & Ng, Szu Hui, 2013. "A sequential approach for stochastic computer model calibration and prediction," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 273-286.
    3. Kim, Wongon & Yoon, Heonjun & Lee, Guesuk & Kim, Taejin & Youn, Byeng D., 2020. "A new calibration metric that considers statistical correlation: Marginal Probability and Correlation Residuals," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    4. Xu, C. & Gertner, G., 2007. "Extending a global sensitivity analysis technique to models with correlated parameters," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5579-5590, August.
    5. Xu, Chonggang & Gertner, George Zdzislaw, 2008. "A general first-order global sensitivity analysis method," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1060-1071.

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