HUP‐BMA: An Integration of Hydrologic Uncertainty Processor and Bayesian Model Averaging for Streamflow Forecasting

Darbandsari, Pedram; Coulibaly, Paulin

doi:10.1029/2020wr029433

Cited by 10 publications

(2 citation statements)

References 96 publications

(175 reference statements)

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“…However, the reliability associated with BMA parameters has not received enough attention. In past literature relevant to the applications of BMA approach, both the weights and variances obtained through the Expectation‐Maximization (EM) algorithm are assigned fixed values (Cao et al., 2021; Darbandsari & Coulibaly, 2021; Dormann et al., 2018; Duan et al., 2007; Huang & Merwade, 2023; Liu & Merwade, 2018; Madadgar & Moradkhani, 2014; Moknatian & Mukundan, 2023; Tsai, 2010). These fixed values cannot represent the uncertainty in the BMA parameters, especially when different data sets are used for training or a specific training data set does encompass the overall prediction capacity of certain models (Madadgar & Moradkhani, 2014; Refsgaard et al., 2012; Rojas et al., 2010; Tebaldi & Knutti, 2007).…”

Section: Introductionmentioning

confidence: 99%

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Huang,

Merwade

2023

Water Resources Research

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As all kinds of numerical models are emerging in hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi‐model methods used to account for various uncertainties in the flood modeling process and generate robust ensemble predictions. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in BMA parameters with fixed values, which are usually obtained from Expectation‐Maximization (EM) algorithm, has not been adequately investigated in BMA‐related applications over the past few decades. Given the limitations of the default EM algorithm, Metropolis‐Hastings (M‐H) algorithm, which is one of the most widely used algorithms in Markov Chain Monte Carlo (MCMC), is proposed to estimate BMA parameters. Both numerical experiments and one‐dimensional Hydrologic Engineering Center‐River Analysis System models are employed to examine the applicability of M‐H algorithm with multiple independent Markov chains. The performances of EM and M‐H algorithms are compared based on the daily water stage predictions from 10 model members. Results show that BMA weights estimated from both algorithms are comparable, while BMA variances obtained from M‐H algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in M‐H algorithm can yield narrower distributions for BMA weights than those from the uniform proposal distribution. Overall, MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its performance is better than the default EM algorithm in terms of both deterministic and probabilistic evaluation metrics as well as algorithm flexibility.

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Section: Introductionmentioning

confidence: 99%

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Huang,

Merwade

2023

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…However, the reliability associated with BMA parameters has not received enough attention. In past literature relevant to the applications of BMA approach, both the weights and variances obtained through the Expectation-Maximization (EM) algorithm are assigned fixed values (Cao et al, 2021;Darbandsari and Coulibaly, 2021;Dormann et al, 2018;Duan et al, 2007;Huang and Merwade, 2023;Liu and Merwade, 2018;Madadgar and Moradkhani, 2014;Moknatian and Mukundan, 2023;Tsai, 2010). These fixed values cannot represent the uncertainty in the BMA parameters, especially when different datasets are used for training or a specific training dataset does encompass the overall prediction capacity of certain models (Madadgar and Moradkhani, 2014;Refsgaard et al, 2012;Rojas et al, 2010;Tebaldi and Knutti, 2007).…”

Section: Introductionmentioning

confidence: 99%

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Huang

Merwade

2023

Preprint

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As all kinds of physics-based and data-driven models are emerging in hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi-model methods used to account for various uncertainty sources in the flood modeling process and generate robust ensemble predictions. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in BMA parameters with fixed values, which are usually obtained from Expectation-Maximization (EM) algorithm, has not been adequately investigated in BMA-related applications over the past few decades. Given the limitations of the commonly used EM algorithm, Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in Markov Chain Monte Carlo (MCMC) method, is proposed to estimate BMA parameters. Both numerical experiments and one-dimensional HEC-RAS models are employed to examine the applicability of M-H algorithm with multiple independent Markov chains. The performances of EM and M-H algorithms are compared based on the daily water stage predictions from 10 model members. Results show that BMA weights estimated from both algorithms are comparable, while BMA variances obtained from M-H algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal used in M-H algorithm can yield narrower distributions for BMA weights than those from the uniform proposal. Overall, MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its performance is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.

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Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling

Rezaei

Ghorbani

Mahjouri

2022

Water Resour Manage

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HUP‐BMA: An Integration of Hydrologic Uncertainty Processor and Bayesian Model Averaging for Streamflow Forecasting

Cited by 10 publications

References 96 publications

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling

Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling

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