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Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models

机译：多元敏感性分析，用于测量动态模型中输入因素的总体贡献

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Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006 [1]) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.
机译：许多动态模型用于生态学和作物科学中的风险评估和决策支持。这样的模型生成时间相关的模型预测，时间可以是离散的或连续的。他们的全局敏感性分析通常分别应用于每个时间输出，但是Campbell等人。 （2006 [1]）提倡在选择的功能基础上对动力学扩展进行全局敏感性分析。本文重点讨论主成分分析与方差分析相结合的特殊情况。除了与主成分相关的指标外，还提出了广义灵敏度指标，以综合每个参数对整个时间序列输出的影响。当输入因子的不确定性是离散的或连续的并且动态模型是离散的或功能性的时，将给出索引定义。基于经典的全局灵敏度分析方法，提出了一种通用的估计算法。该方法适用于具有13个不确定参数的动态小麦作物模型。比较了三种全局灵敏度分析方法：Sobol'-Saltelli方法，扩展FAST方法和分辨率6的分数阶乘设计。

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