Foglia L, Mehl S W
U.S. Geological Survey, Boulder, CO 80303.
Ground Water. 2015 Jan-Feb;53(1):130-9. doi: 10.1111/gwat.12144. Epub 2013 Dec 10.
In this work, we provide suggestions for designing experiments where calibration of many models is required and guidance for identifying problematic calibrations. Calibration of many conceptual models which have different representations of the physical processes in the system, as is done in cross-validation studies or multi-model analysis, often uses computationally frugal inversion techniques to achieve tractable execution times. However, because these frugal methods are usually local methods, and the inverse problem is almost always nonlinear, there is no guarantee that the optimal solution will be found. Furthermore, evaluation of each inverse model's performance to identify poor calibrations can be tedious. Results of this study show that if poorly calibrated models are included in the analysis, simulated predictions and measures of prediction uncertainty can be affected in unexpected ways. Guidelines are provided to help identify problematic regressions and correct them.
在这项工作中，我们针对需要对多个模型进行校准的实验设计提供了建议，并为识别有问题的校准提供了指导。正如在交叉验证研究或多模型分析中所做的那样，对许多在系统中对物理过程有不同表示的概念模型进行校准，通常会使用计算成本较低的反演技术来实现可处理的执行时间。然而，由于这些低成本方法通常是局部方法，并且反问题几乎总是非线性的，因此无法保证找到最优解。此外，评估每个反演模型的性能以识别校准不佳的情况可能会很繁琐。本研究结果表明，如果分析中包含校准不佳的模型，模拟预测和预测不确定性的度量可能会受到意想不到的影响。我们提供了指导方针，以帮助识别有问题的回归并进行纠正。
