Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.jhydrol.2017.12.071 |
A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model | |
Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.; Amerjeed, Mansoor | |
通讯作者 | Chen, Mingjie |
来源期刊 | JOURNAL OF HYDROLOGY
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ISSN | 0022-1694 |
EISSN | 1879-2707 |
出版年 | 2018 |
卷号 | 557页码:826-837 |
英文摘要 | Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol’ method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system. (C) 2018 Elsevier B.V. All rights reserved. |
英文关键词 | MODFLOW Bayesian Inverse modeling Surrogate Bagging MARS |
类型 | Article |
语种 | 英语 |
国家 | Oman |
收录类别 | SCI-E |
WOS记录号 | WOS:000425077300064 |
WOS关键词 | MONTE-CARLO-SIMULATION ; SEAWATER INTRUSION ; GEOTHERMAL PROSPECT ; OPTIMIZATION ; CO2 ; UNCERTAINTY ; FRAMEWORK ; IMPACTS |
WOS类目 | Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources |
WOS研究方向 | Engineering ; Geology ; Water Resources |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/211048 |
作者单位 | Sultan Qaboos Univ, Water Res Ctr, Muscat, Oman |
推荐引用方式 GB/T 7714 | Chen, Mingjie,Izady, Azizallah,Abdalla, Osman A.,et al. A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model[J],2018,557:826-837. |
APA | Chen, Mingjie,Izady, Azizallah,Abdalla, Osman A.,&Amerjeed, Mansoor.(2018).A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model.JOURNAL OF HYDROLOGY,557,826-837. |
MLA | Chen, Mingjie,et al."A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model".JOURNAL OF HYDROLOGY 557(2018):826-837. |
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