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.
翻译:本文为水文模型的不确定性量化和巴伊西亚校准提供了一个高效的替代模型战略,特别是考虑一个基于过程的动态城市排水模拟器,预测降水事件期间集水区排放的情况。案例研究的目的是进行全球敏感性分析,确定未知模型参数以及测量和预测错误。这些目标只能通过降低计算成本,即降低必要模型运行的数量来实现。为此,建议采用定期利用的元模型技术,以便快速量化不确定性。主要组成部分分析用于减少产出的维度,稀有的多种族混乱扩大用于模拟减少的产出。Sobol的灵敏指数直接通过后处理从扩展系数中获取。通过Markov链 Monte Carlo 后台取样,Bayesian的推断速度急剧加快。