.png){kind=icon}
返回
Technical Note: Improved sampling of behavioral subsurface flow model parameters using active subspaces
.png){kind=icon}
OA
AI
DOI：10.5194/hess-24-4567-2020.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In global sensitivity analysis and ensemble-based model calibration, it is essential to create a large enough sample of model simulations with different parameters that all yield plausible model results. This can be difficult if a priori plausible parameter combinations frequently yield non-behavioral model results. In a previous study (Erdal and Cirpka, 2019), we developed and tested a parameter-sampling scheme based on active-subspace decomposition. While in principle this scheme worked well, it still implied testing a substantial fraction of parameter combinations that ultimately had to be discarded because of implausible model results. This technical note presents an improved sampling scheme and illustrates its simplicity and efficiency by a small test case. The new sampling scheme can be tuned to either outperform the original implementation by improving the sampling efficiency while maintaining the accuracy of the result or by improving the accuracy of the result while maintaining the sampling efficiency.
Keyword:
GLOBAL SENSITIVITY-ANALYSIS
CHAIN MONTE-CARLO
UNCERTAINTY
REDUCTION
期刊
IF:
5.8
论文数:
6.0K
被引数:
2.8W
机构
引用论文
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications水文建模中的全球敏感性分析: 概念，方法，理论框架和应用综述.png)
JOURNAL OF HYDROLOGY
OAAI
JOURNAL OF HYDROLOGYIF6.3
OAAI分享收藏
查看更多内容