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首页> 外文期刊>Natural Hazards >Dynamic sensitivity analysis of long-running landslide models through basis set expansion and meta-modelling
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Dynamic sensitivity analysis of long-running landslide models through basis set expansion and meta-modelling

机译：通过基集扩展和元建模对长期滑坡模型进行动态灵敏度分析

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Predicting the temporal evolution of landslides is typically supported by numerical modelling. Dynamic sensitivity analysis aims at assessing the influence of the landslide properties on the time-dependent predictions (e.g. time series of landslide displacements). Yet, two major difficulties arise: (1) Global sensitivity analysis require running the landslide model a high number of times (> 1,000), which may become impracticable when the landslide model has a high computation time cost (> several hours); (2) Landslide model outputs are not scalar, but function of time, that is, they are n-dimensional vectors with n usually ranging from 100 to 1,000. In this article, I explore the use of a basis set expansion, such as principal component analysis, to reduce the output dimensionality to a few components, each of them being interpreted as a dominant mode of variation in the overall structure of the temporal evolution. The computationally intensive calculation of the Sobol' indices for each of these components are then achieved through meta-modelling, that is, by replacing the landslide model by a "costless-to-evaluate" approximation (e.g. a projection pursuit regression model). The methodology combining "basis set expansion-meta-model-Sobol' indices" is then applied to the Swiss La Frasse landslide to investigate the dynamic sensitivity analysis of the surface horizontal displacements to the slip surface properties during the pore pressure changes. I show how to extract information on the sensitivity of each main modes of temporal behaviour using a limited number (a few tens) of long-running simulations
机译：数值建模通常可以预测滑坡的时间演变。动态灵敏度分析旨在评估滑坡特性对时间相关预测（例如滑坡位移的时间序列）的影响。然而，出现了两个主要困难：（1）全局敏感性分析要求运行大量的滑坡模型（> 1,000），而当滑坡模型的计算时间成本较高（>几个小时）时，这可能变得不可行; （2）滑坡模型的输出不是标量，而是时间的函数，也就是说，它们是n维向量，n通常在100到1,000之间。在本文中，我探索了基集扩展（例如主成分分析）的使用，以将输出维数减少为几个成分，每个成分都被解释为时间演化整体结构中的主要变异模式。然后，通过元建模，即通过用“无成本估算”近似（例如，投影追踪回归模型）代替滑坡模型，来实现这些要素中每个要素的Sobol指数的计算量大的计算。然后，将结合“基本集扩展-元模型-Sobol指数”的方法应用于瑞士La Frasse滑坡，以研究孔隙压力变化过程中表面水平位移对滑动表面特性的动态敏感性分析。我展示了如何使用有限（数十个）长时间运行的模拟来提取有关时间行为的每个主要模式的敏感性的信息

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