Bayesian MCMC calibration and uncertainty analysis for computationally expensive models is implemented using the SOARS (Statistical and Optimization Analysis using Response Surfaces) methodology. SOARS uses a radial basis function interpolator as a surrogate, also known as an emulator or meta-model, for the logarithm of the posterior density. To prevent wasteful evaluations of the expensive model, the emulator is built only on a high posterior density region (HPDR), which is located by a global optimization algorithm. The set of points in the HPDR where the expensive model is evaluated is determined sequentially by the GRIMA algorithm described in detail in another paper but outlined here. Enhancements of the GRIMA algorithm were introduced to improve efficiency. A case study uses an eight-parameter SWAT2005 (Soil and Water Assessment Tool) model where daily stream flows and phosphorus concentrations are modeled for the Town Brook watershed which is part of the New York City water supply. A Supplemental Material file available online contains additional technical details and additional analysis of the Town Brook application.

中文翻译：

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具有多个输出的计算昂贵模型的不确定性分析。


使用 SOARS（使用响应面的统计和优化分析）方法对计算成本较高的模型进行贝叶斯 MCMC 校准和不确定性分析。 SOARS 使用径向基函数插值器作为后验密度对数的替代项（也称为模拟器或元模型）。为了防止对昂贵模型的浪费评估，模拟器仅构建在高后验密度区域（HPDR）上，该区域由全局优化算法定位。 HPDR 中评估昂贵模型的点集由 GRIMA 算法按顺序确定，该算法在另一篇论文中详细描述，但在此概述。引入了 GRIMA 算法的增强功​​能以​​提高效率。案例研究使用八参数 SWAT2005（土壤和水评估工具）模型，其中对纽约市供水系统的 Town Brook 流域的每日溪流流量和磷浓度进行建模。在线提供的补充材料文件包含 Town Brook 应用程序的附加技术细节和附加分析。