Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR022658 |
Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations | |
Zhang, Jiangjiang1; Man, Jun1; Lin, Guang2,3; Wu, Laosheng4; Zeng, Lingzao1 | |
2018-07-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:7页码:4867-4886 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | Markov chain Monte Carlo (MCMC) simulation methods are widely used to assess parametric uncertainties of hydrologic models conditioned on measurements of observable state variables. However, when the model is CPU-intensive and high dimensional, the computational cost of MCMC simulation will be prohibitive. In this situation, a CPU-efficient while less accurate low-fidelity model (e.g., a numerical model with a coarser discretization or a data-driven surrogate) is usually adopted. Nowadays, multifidelity simulation methods that can take advantage of both the efficiency of the low-fidelity model and the accuracy of the high-fidelity model are gaining popularity. In the MCMC simulation, as the posterior distribution of the unknown model parameters is the region of interest, it is wise to distribute most of the computational budget (i.e., the high-fidelity model evaluations) therein. Based on this idea, in this paper we propose an adaptive multifidelity MCMC algorithm for efficient inverse modeling of hydrologic systems. In this method, we evaluate the high-fidelity model mainly in the posterior region through iteratively running MCMC based on a Gaussian process system that is adaptively constructed with multifidelity simulation. The error of the Gaussian process system is rigorously considered in the MCMC simulation and gradually reduced to a negligible level in the posterior region. Thus, the proposed method can obtain an accurate estimate of the posterior distribution with a small number of the high-fidelity model evaluations. The performance of the proposed method is demonstrated by three numerical case studies in inverse modeling of hydrologic systems. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000442502100038 |
WOS关键词 | BAYESIAN EXPERIMENTAL-DESIGN ; GAUSSIAN PROCESS REGRESSION ; ENSEMBLE KALMAN FILTER ; DATA ASSIMILATION ; GROUNDWATER-FLOW ; HYDRAULIC CONDUCTIVITY ; PARAMETER-ESTIMATION ; POROUS-MEDIA ; EFFICIENT ; UNCERTAINTY |
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/21518 |
专题 | 资源环境科学 |
作者单位 | 1.Zhejiang Univ, Coll Environm & Resource Sci, Inst Soil & Water Resources & Environm Sci, Zhejiang Prov Key Lab Agr Resources & Environm, Hangzhou, Zhejiang, Peoples R China; 2.Purdue Univ, Dept Math, W Lafayette, IN 47907 USA; 3.Purdue Univ, Sch Mech Engn, W Lafayette, IN 47907 USA; 4.Univ Calif Riverside, Dept Environm Sci, Riverside, CA 92521 USA |
推荐引用方式 GB/T 7714 | Zhang, Jiangjiang,Man, Jun,Lin, Guang,et al. Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations[J]. WATER RESOURCES RESEARCH,2018,54(7):4867-4886. |
APA | Zhang, Jiangjiang,Man, Jun,Lin, Guang,Wu, Laosheng,&Zeng, Lingzao.(2018).Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations.WATER RESOURCES RESEARCH,54(7),4867-4886. |
MLA | Zhang, Jiangjiang,et al."Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations".WATER RESOURCES RESEARCH 54.7(2018):4867-4886. |
条目包含的文件 | 条目无相关文件。 |
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