Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis
Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis
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DOI：
10.1016/j.envsoft.2010.10.007
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发表时间：
2011-04
期刊：
影响因子：
--
通讯作者：
J. Yang
中科院分区：
文献类型：
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作者：
J. Yang
敏感度分析在模型开发、校准、不确定性分析、情景分析以及决策过程中发挥着重要作用。随着不同的灵敏度分析技术的出现，选择合适的技术、监测灵敏度指数的收敛和估计灵敏度指数的不确定性对于环境建模是非常关键的，特别是对于分布式模型，因为它们具有高度的非线性、非单调性、参数高度相关性和密集的计算要求。确定某些技术在计算要求、可靠性和其他标准方面是否优于其他技术将是有用的。本文提出了两种灵敏度分析技术的收敛监测和不确定度估计方法。一种是基于中心极限定理，另一种是基于Bootstrap技术。这两种方法被用来评估应用于环境模型的五种不同的敏感性分析技术。这些技术是：Sobol方法、Morris方法、线性回归(LR)、区域化敏感性分析(RSA)和非参数平滑。结果表明：(I)尽管进行了大量的模型评估，但Sobol方法在量化敏感性和对参数进行排序方面是非常稳健的；(Ii)Morris方法对于以中等代价对不重要的参数进行排序是有效的；(Iii)非参数平滑在量化主效应和低阶交互作用方面是可靠和稳健的，但需要少量的模型评估；(Iv)另外两种技术，即LR和RSA，应该谨慎使用。
Sensitivity analysis plays an important role in model development, calibration, uncertainty analysis, scenario analysis, and, hence, decision making. With the availability of different sensitivity analysis techniques, selecting an appropriate technique, monitoring the convergence and estimating the uncertainty of the sensitivity indices are very crucial for environmental modelling, especially for distributed models due to their high non-linearity, non-monotonicity, highly correlated parameters, and intensive computational requirements. It would be useful to identify whether some techniques outperform others with respect to computational requirements, reliability, and other criteria. This paper proposes two methods to monitor the convergence and estimate the uncertainty of sensitivity analysis techniques. One is based on the central limit theorem and the other on the bootstrap technique. These two methods are implemented to assess five different sensitivity analysis techniques applied to an environmental model. These techniques are: the Sobol’ method, the Morris method, Linear Regression (LR), Regionalized Sensitivity Analysis (RSA), and non-parametric smoothing. The results show that: (i) the Sobol’ method is very robust in quantifying sensitivities and ranking parameters despite a large number of model evaluations; (ii) the Morris method is efficient to rank out unimportant parameters at a medium cost; (iii) the non-parametric smoothing is reliable and robust in quantifying the main effects and low-order interactions while requiring a small number of model evaluations; finally (iv) the other two techniques, that is, LR and RSA, should be used with care.