Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR019831 |
Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model | |
Xu, Tianfang1,2; Valocchi, Albert J.1; Ye, Ming3; Liang, Feng4 | |
2017-05-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:5 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Groundwater model structural error is ubiquitous, due to simplification and/or misrepresentation of real aquifer systems. During model calibration, the basic hydrogeological parameters may be adjusted to compensate for structural error. This may result in biased predictions when such calibrated models are used to forecast aquifer responses to new forcing. We investigate the impact of model structural error on calibration and prediction of a real-world groundwater flow model, using a Bayesian method with a data-driven error model to explicitly account for model structural error. The error-explicit Bayesian method jointly infers model parameters and structural error and thereby reduces parameter compensation. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models (based on machine learning techniques) as a substitute for the computationally expensive groundwater model. We demonstrate that with explicit treatment of model structural error, the Bayesian method yields parameter posterior distributions that are substantially different from those derived using classical Bayesian calibration that does not account for model structural error. We also found that the error-explicit Bayesian method gives significantly more accurate prediction along with reasonable credible intervals. Finally, through variance decomposition, we provide a comprehensive assessment of prediction uncertainty contributed from parameter, model structure, and measurement uncertainty. The results suggest that the error-explicit Bayesian approach provides a solution to real-world modeling applications for which data support the presence of model structural error, yet model deficiency cannot be specifically identified or corrected. |
英文关键词 | Bayesian calibration uncertainty decomposition model structural error surrogate modeling groundwater |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000403712100034 |
WOS关键词 | MONTE-CARLO-SIMULATION ; GLOBAL SENSITIVITY-ANALYSIS ; SUPPORT VECTOR MACHINES ; HYDROLOGICAL UNCERTAINTY ; REGRESSION ; PREDICTION ; FRAMEWORK ; INFERENCE ; FORESTS ; DESIGN |
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/21779 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Illinois, Dept Civil & Environm Engn, Urbana, IL 61801 USA; 2.Michigan State Univ, Dept Earth & Environm Sci, E Lansing, MI 48824 USA; 3.Florida State Univ, Dept Comp Sci, Tallahassee, FL 32306 USA; 4.Univ Illinois, Dept Stat, Urbana, IL USA |
推荐引用方式 GB/T 7714 | Xu, Tianfang,Valocchi, Albert J.,Ye, Ming,et al. Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model[J]. WATER RESOURCES RESEARCH,2017,53(5). |
APA | Xu, Tianfang,Valocchi, Albert J.,Ye, Ming,&Liang, Feng.(2017).Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model.WATER RESOURCES RESEARCH,53(5). |
MLA | Xu, Tianfang,et al."Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model".WATER RESOURCES RESEARCH 53.5(2017). |
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