Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm
Abstract
This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.
- Authors:
-
- Beijing Normal Univ., Beijing (China)
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Chinese Research Academy of Environmental Sciences, Beijing (China)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Natural Science Foundation of China (NSFC)
- OSTI Identifier:
- 1371944
- Alternate Identifier(s):
- OSTI ID: 1396589
- Grant/Contract Number:
- AC02-06CH11357; 51479003; 51279006
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Hydrology
- Additional Journal Information:
- Journal Volume: 549; Journal Issue: C; Journal ID: ISSN 0022-1694
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; Bayesian approach; Metropolis–Hastings Markov Chain Monte Carlo algorithm; maximum likelihood estimation; uncertainty analysis
Citation Formats
Wang, Hongrui, Wang, Cheng, Wang, Ying, Gao, Xiong, and Yu, Chen. Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm. United States: N. p., 2017.
Web. doi:10.1016/j.jhydrol.2017.03.073.
Wang, Hongrui, Wang, Cheng, Wang, Ying, Gao, Xiong, & Yu, Chen. Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm. United States. https://doi.org/10.1016/j.jhydrol.2017.03.073
Wang, Hongrui, Wang, Cheng, Wang, Ying, Gao, Xiong, and Yu, Chen. Wed .
"Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm". United States. https://doi.org/10.1016/j.jhydrol.2017.03.073. https://www.osti.gov/servlets/purl/1371944.
@article{osti_1371944,
title = {Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm},
author = {Wang, Hongrui and Wang, Cheng and Wang, Ying and Gao, Xiong and Yu, Chen},
abstractNote = {This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.},
doi = {10.1016/j.jhydrol.2017.03.073},
journal = {Journal of Hydrology},
number = C,
volume = 549,
place = {United States},
year = {Wed Apr 05 00:00:00 EDT 2017},
month = {Wed Apr 05 00:00:00 EDT 2017}
}
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