Abstract Decision of emergency response to releases of hazardous material in the atmosphere increasingly rely on numerical simulations. This paper presents two contributions for accounting for the uncertainty inherent to those simulations. We first focused on one way of modelling these uncertainties, namely by applying stochastic perturbations to the inputs of the numerical dispersion model. We devised a generic mathematical formulation for time dependent perturbation of both amplitude and dynamics of the inputs. It allows a more thorough exploration of possible outcomes than simpler perturbations found in the literature. We then improved on the current state of the art on dimension reduction of atmospheric data. Indeed, most statistical methods cannot cope with high dimensional data such as the maps simulated with atmospheric dispersion models. Principal component analysis, the most widely used method for dimension reduction, relies on a linearity hypothesis that is not verified by these sets of maps. We conducted a very encouraging experiment with auto-associative models, a non-linear extension of this method.

中文翻译：

---

用于模拟大气扩散模拟不确定性的随机扰动和降维

摘要 对大气中有害物质释放的应急响应决策越来越依赖于数值模拟。本文提出了两个贡献来解释这些模拟固有的不确定性。我们首先关注对这些不确定性进行建模的一种方法，即通过将随机扰动应用于数值分散模型的输入。我们为输入的幅度和动态的时间相关扰动设计了一个通用的数学公式。与文献中发现的简单扰动相比，它允许对可能的结果进行更彻底的探索。然后，我们改进了大气数据降维的最新技术。事实上，大多数统计方法无法处理高维数据，例如用大气扩散模型模拟的地图。主成分分析是最广泛使用的降维方法，它依赖于未被这些映射集验证的线性假设。我们使用自动关联模型进行了一项非常令人鼓舞的实验，这是该方法的非线性扩展。