Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR023589 |
Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models | |
Dai, Heng1,2; Chen, Xingyuan1; Ye, Ming3; Song, Xuehang1; Hammond, Glenn4; Hu, Bill2; Zachara, John M.1 | |
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:3541-3555 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Peoples R China |
英文摘要 | Sensitivity analysis is a vital tool in numerical modeling to identify important parameters and processes that contribute to the overall uncertainty in model outputs. We developed a new sensitivity analysis method to quantify the relative importance of uncertain model processes that contain multiple uncertain parameters. The method is based on the concepts of Bayesian networks (BNs) to account for complex hierarchical uncertainty structure of a model system. We derived a new set of sensitivity indices using the methodology of variance-based global sensitivity analysis with the Bayesian inference. The framework is capable of representing the detailed uncertainty information of a complex model system using BNs and affords flexible grouping of different uncertain inputs given their characteristics and dependency structures. We have implemented the method on a real-world biogeochemical model at the groundwater-surface water interface within the Hanford Site's 300 Area. The uncertainty sources of the model were first grouped into forcing scenario and three different processes based on our understanding of the complex system. The sensitivity analysis results indicate that both the reactive transport and groundwater flow processes are important sources of uncertainty for carbon-consumption predictions. Within the groundwater flow process, the structure of geological formations is more important than the permeability heterogeneity within a given geological formation. Our new sensitivity analysis framework based on BNs offers substantial flexibility for investigating the importance of combinations of interacting uncertainty sources in a hierarchical order, and it is expected to be applicable to a wide range of multiphysics models for complex systems. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000468597900052 |
WOS关键词 | DOMINANT PROCESSES CONCEPT ; GLOBAL SENSITIVITY ; AQUIFER CHARACTERIZATION ; GROUNDWATER-FLOW ; TRANSPORT ; HYDROLOGY ; FRAMEWORK |
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/182257 |
专题 | 资源环境科学 |
作者单位 | 1.Pacific Northwest Natl Lab, Richland, WA 99352 USA; 2.Jinan Univ, Inst Groundwater & Earth Sci, Guangzhou, Guangdong, Peoples R China; 3.Florida State Univ, Dept Earth Ocean & Atmospher Sci, Tallahassee, FL 32306 USA; 4.Sandia Natl Labs, POB 5800, Albuquerque, NM 87185 USA |
推荐引用方式 GB/T 7714 | Dai, Heng,Chen, Xingyuan,Ye, Ming,et al. Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models[J]. WATER RESOURCES RESEARCH,2019,55(4):3541-3555. |
APA | Dai, Heng.,Chen, Xingyuan.,Ye, Ming.,Song, Xuehang.,Hammond, Glenn.,...&Zachara, John M..(2019).Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models.WATER RESOURCES RESEARCH,55(4),3541-3555. |
MLA | Dai, Heng,et al."Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models".WATER RESOURCES RESEARCH 55.4(2019):3541-3555. |
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