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Title: 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 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 themore » 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.« less

Authors:
 [1];  [1];  [2];  [1]; ORCiD logo [3];  [2];  [1]
  1. BATTELLE (PACIFIC NW LAB)
  2. Florida State University
  3. Sandia National Laboratory
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1543285
Report Number(s):
PNNL-SA-141922
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 55; Journal Issue: 4
Country of Publication:
United States
Language:
English

Citation Formats

Dai, Heng, Chen, Xingyuan, Ye, Ming, Song, Xuehang, Hammond, Glenn, Hu, Bill X., 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 X., & Zachara, John M. Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models. United States. doi:10.1029/2018WR023589.
Dai, Heng, Chen, Xingyuan, Ye, Ming, Song, Xuehang, Hammond, Glenn, Hu, Bill X., and Zachara, John M. Mon . "Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models". United States. doi:10.1029/2018WR023589.
@article{osti_1543285,
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 X. 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 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 = {2019},
month = {4}
}

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