Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR023382 |
Model Variable Augmentation (MVA) for Diagnostic Assessment of sensitivity Analysis Results | |
Mai, Juliane; Tolson, Bryan A. | |
2019-04-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:4页码:2631-2651 |
文章类型 | Article |
语种 | 英语 |
国家 | Canada |
英文摘要 | Sensitivity analysis (SA) is a critical part in the construction of all models, including environmental and water resources simulation models. For example, SA functions to characterize which model inputs the model outputs are overly sensitive or insensitive to. However, the quality of SA results is rarely assessed. If assessed, bootstrapping of the sensitivity results is used to determine the reliability of the SA output. Bootstrapping, however, is known to be inappropriate with small sample sizes. In contrast, increasing model computational burdens continues to drive researchers to apply existing SA techniques and develop new ones, with smaller and smaller sample sizes. The new Model Variable Augmentation (MVA) approach is therefore introduced here to assess the quality of SA results without performing any additional model runs or requiring bootstrapping. MVA augments the original model input variables with additional variables of known properties. The sensitivities of the augmented model variables are used to draw conclusions on the reliability of the other "original" model parameters' sensitivities. The MVA method is applied to two global SA methods: the variance-based Sobol' method and the moment-independent PAWN method. MVA is scrutinized using analytical benchmark functions and then used to quality check sensitivity results of two hydrologic models. Results show the following: (1) MVA is a framework to quality check the implementation of a SA method; (2) for Sobol' and PAWN analyses, MVA-assisted ranking of input sensitivity measures outperforms the standard ranking procedure without MVA; and (3) MVA provides reasonable estimation of the uncertainty of sensitivity estimates. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000468597900005 |
WOS关键词 | GLOBAL SENSITIVITY ; PARAMETER SENSITIVITY ; UNCERTAINTY ; FRAMEWORK ; CONVERGENCE ; CALIBRATION ; DESIGN ; ROBUST |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/182210 |
专题 | 资源环境科学 |
作者单位 | Univ Waterloo, Dept Civil & Environm Engn, Waterloo, ON, Canada |
推荐引用方式 GB/T 7714 | Mai, Juliane,Tolson, Bryan A.. Model Variable Augmentation (MVA) for Diagnostic Assessment of sensitivity Analysis Results[J]. WATER RESOURCES RESEARCH,2019,55(4):2631-2651. |
APA | Mai, Juliane,&Tolson, Bryan A..(2019).Model Variable Augmentation (MVA) for Diagnostic Assessment of sensitivity Analysis Results.WATER RESOURCES RESEARCH,55(4),2631-2651. |
MLA | Mai, Juliane,et al."Model Variable Augmentation (MVA) for Diagnostic Assessment of sensitivity Analysis Results".WATER RESOURCES RESEARCH 55.4(2019):2631-2651. |
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