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Title: Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method: FAILURE PROBABILITY

Abstract

In decision-making for groundwater management and contamination remediation, it is important to accurately evaluate the probability of the occurrence of a failure event. For small failure probability analysis, a large number of model evaluations are needed in the Monte Carlo (MC) simulation, which is impractical for CPU-demanding models. One approach to alleviate the computational cost caused by the model evaluations is to construct a computationally inexpensive surrogate model instead. However, using a surrogate approximation can cause an extra error in the failure probability analysis. Moreover, constructing accurate surrogates is challenging for high-dimensional models, i.e., models containing many uncertain input parameters. To address these issues, we propose an efficient two-stage MC approach for small failure probability analysis in high-dimensional groundwater contaminant transport modeling. In the first stage, a low-dimensional representation of the original high-dimensional model is sought with Karhunen–Loève expansion and sliced inverse regression jointly, which allows for the easy construction of a surrogate with polynomial chaos expansion. Then a surrogate-based MC simulation is implemented. In the second stage, the small number of samples that are close to the failure boundary are re-evaluated with the original model, which corrects the bias introduced by the surrogate approximation. The proposed approach is testedmore » with a numerical case study and is shown to be 100 times faster than the traditional MC approach in achieving the same level of estimation accuracy.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [1]; ORCiD logo [4]
  1. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China
  2. Pacific Northwest National Laboratory, Richland Washington USA
  3. Department of Mathematics and School of Mechanical Engineering, Purdue University, West Lafayette Indiana USA
  4. Department of Environmental Sciences, University of California, Riverside California USA
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1356505
Report Number(s):
PNNL-SA-122101
Journal ID: ISSN 0043-1397
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Water Resources Research; Journal Volume: 53; Journal Issue: 3
Country of Publication:
United States
Language:
English

Citation Formats

Zhang, Jiangjiang, Li, Weixuan, Lin, Guang, Zeng, Lingzao, and Wu, Laosheng. Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method: FAILURE PROBABILITY. United States: N. p., 2017. Web. doi:10.1002/2016WR019518.
Zhang, Jiangjiang, Li, Weixuan, Lin, Guang, Zeng, Lingzao, & Wu, Laosheng. Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method: FAILURE PROBABILITY. United States. doi:10.1002/2016WR019518.
Zhang, Jiangjiang, Li, Weixuan, Lin, Guang, Zeng, Lingzao, and Wu, Laosheng. Wed . "Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method: FAILURE PROBABILITY". United States. doi:10.1002/2016WR019518.
@article{osti_1356505,
title = {Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method: FAILURE PROBABILITY},
author = {Zhang, Jiangjiang and Li, Weixuan and Lin, Guang and Zeng, Lingzao and Wu, Laosheng},
abstractNote = {In decision-making for groundwater management and contamination remediation, it is important to accurately evaluate the probability of the occurrence of a failure event. For small failure probability analysis, a large number of model evaluations are needed in the Monte Carlo (MC) simulation, which is impractical for CPU-demanding models. One approach to alleviate the computational cost caused by the model evaluations is to construct a computationally inexpensive surrogate model instead. However, using a surrogate approximation can cause an extra error in the failure probability analysis. Moreover, constructing accurate surrogates is challenging for high-dimensional models, i.e., models containing many uncertain input parameters. To address these issues, we propose an efficient two-stage MC approach for small failure probability analysis in high-dimensional groundwater contaminant transport modeling. In the first stage, a low-dimensional representation of the original high-dimensional model is sought with Karhunen–Loève expansion and sliced inverse regression jointly, which allows for the easy construction of a surrogate with polynomial chaos expansion. Then a surrogate-based MC simulation is implemented. In the second stage, the small number of samples that are close to the failure boundary are re-evaluated with the original model, which corrects the bias introduced by the surrogate approximation. The proposed approach is tested with a numerical case study and is shown to be 100 times faster than the traditional MC approach in achieving the same level of estimation accuracy.},
doi = {10.1002/2016WR019518},
journal = {Water Resources Research},
number = 3,
volume = 53,
place = {United States},
year = {Wed Mar 01 00:00:00 EST 2017},
month = {Wed Mar 01 00:00:00 EST 2017}
}