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首页> 外文期刊>Reliability Engineering & System Safety >Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation
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Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation

机译：用于全局灵敏度分析和模型校准的主成分分析和稀疏多项式混沌展开：在城市排水模拟中的应用

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This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dynamical urban drainage simulator that predicts the discharge from a catchment area during a precipitation event is considered. The goal of the case study is to perform a global sensitivity analysis and to identify the unknown model parameters as well as the measurement and prediction errors. These objectives can only be achieved by cheapening the incurred computational costs, that is, lowering the number of necessary model runs. With this in mind, a regularity-exploiting metamodeling technique is proposed that enables fast uncertainty quantification. Principal component analysis is used for output dimensionality reduction and sparse polynomial chaos expansions are used for the emulation of the reduced outputs. Sobol' sensitivity indices are obtained directly from the expansion coefficients by a mere post-processing. Bayesian inference via Markov chain Monte Carlo posterior sampling is drastically accelerated.
机译：本文提出了一种有效的替代建模策略，用于水文模型的不确定性量化和贝叶斯校准。特别是考虑了一种基于过程的动态城市排水模拟器，该模拟器可预测降雨事件期间集水区的排放量。案例研究的目的是执行全局敏感性分析，并识别未知的模型参数以及测量和预测误差。这些目标只能通过降低产生的计算成本（即减少必要的模型运行次数）来实现。考虑到这一点，提出了一种利用规律性的元建模技术，可以快速进行不确定性量化。主成分分析用于减少输出维数，稀疏多项式混沌展开用于简化输出的仿真。 Sobol的灵敏度指数可通过仅后处理直接从膨胀系数获得。通过马尔可夫链蒙特卡洛后验采样的贝叶斯推断得到了极大的加速。

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