Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models
Abstract
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 here 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 thanmore »
- Authors:
-
[1];
[2];
[3];
[2];
[4];
[5];
[2]
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Jinan Univ., Guangzhou (China). Inst. of Groundwater and Earth Sciences
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Florida State Univ., Tallahassee, FL (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Jinan Univ., Guangzhou (China)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Florida State Univ., Tallahassee, FL (United States); Jinan Univ., Guangzhou (China)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); PNNL Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF); National Natural Science Foundation of China (NSFC)
- OSTI Identifier:
- 1542940
- Alternate Identifier(s):
- OSTI ID: 1502972; OSTI ID: 1506160
- Report Number(s):
- SAND-2019-2924J
Journal ID: ISSN 0043-1397
- Grant/Contract Number:
- SC0008272; SC0019438; AC05‐76RL01830; AC02‐05CH11231; NA0003525; AC05-76RL01830; AC02-05CH11231; 1552329; 41807182; SBR PNNL SFA
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Volume: 55; Journal Issue: 4; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; Bayesian network; hierarchical sensitivity analysis; biogeochemical modeling; reactive transport
Citation Formats
Dai, Heng, Chen, Xingyuan, Ye, Ming, Song, Xuehang, Hammond, Glenn, Hu, Bill, and Zachara, John M. Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models. United States: N. p., 2019.
Web. doi:10.1029/2018WR023589.
Dai, Heng, Chen, Xingyuan, Ye, Ming, Song, Xuehang, Hammond, Glenn, Hu, Bill, & Zachara, John M. Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models. United States. https://doi.org/10.1029/2018WR023589
Dai, Heng, Chen, Xingyuan, Ye, Ming, Song, Xuehang, Hammond, Glenn, Hu, Bill, and Zachara, John M. Fri .
"Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models". United States. https://doi.org/10.1029/2018WR023589. https://www.osti.gov/servlets/purl/1542940.
@article{osti_1542940,
title = {Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models},
author = {Dai, Heng and Chen, Xingyuan and Ye, Ming and Song, Xuehang and Hammond, Glenn and Hu, Bill and Zachara, John M.},
abstractNote = {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 here 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 multi-physics models for complex systems.},
doi = {10.1029/2018WR023589},
journal = {Water Resources Research},
number = 4,
volume = 55,
place = {United States},
year = {Fri Mar 15 00:00:00 EDT 2019},
month = {Fri Mar 15 00:00:00 EDT 2019}
}
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