Uncertainty Analysis and Quantification in Flood Insurance Rate Maps Using Bayesian Model Averaging and Hierarchical BMA

Huang, Tao; Merwade, Venkatesh

doi:10.1061/jhyeff.heeng-5851

Cited by 9 publications

(21 citation statements)

References 64 publications

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“…Previously researched applications using these methods include weather forecasting (Raftery et al, 2005), flood insurance rate maps (Huang & Merwade, 2023), risk assessment in debris flow (Tian et al, 2023), and crop management (Fei et al, 2023).…”

Section: Combinationmentioning

confidence: 99%

Multi-View Ensemble Learning With Missing Data: Computational Framework and Evaluations using Novel Data from the Safe Autonomous Driving Domain

Greer¹,

Lulua²,

Gopalkrishnan³

et al. 2023

Preprint

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Real-world applications with multiple sensors observing an event are expected to make continuously-available predictions, even in cases where information may be intermittently missing. We explore methods in ensemble learning and sensor fusion to make use of redundancy and information shared between four camera views, applied to the task of hand activity classification for autonomous driving. In particular, we show that a late-fusion approach between parallel convolutional neural networks can outperform even the best-placed single camera model. To enable this approach, we propose a scheme for handling missing information, and then provide comparative analysis of this late-fusion approach to additional methods such as weighted majority voting and model combination schemes.

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Section: Combinationmentioning

confidence: 99%

Multi-View Ensemble Learning With Missing Data: Computational Framework and Evaluations using Novel Data from the Safe Autonomous Driving Domain

Greer¹,

Lulua²,

Gopalkrishnan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, "equifinality" in hydrologic and hydraulic modeling may lead to multiple models or different model configurations to yield similar results that match the observations equally well (Beven and Binley, 1992;Refsgaard et al, 2012;Von Bertalanffy, 1972). Thus, it is not recommended to rely on streamflow predictions and flood inundation maps obtained by a single model implementation (Duan et al, 2007;Huang and Merwade, 2023;Liu and Merwade, 2018;Romanowicz and Beven, 2003;Zounemat-Kermani et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Among these multi-model ensemble approaches, the Bayesian model averaging (BMA) approach (Kass and Raftery, 1995;Leamer, 1978;Raftery et al, 2005) has been successfully applied to the flood modeling (Duan et al, 2007;Huang and Merwade, 2023;Merwade, 2018, 2019;Rings et al, 2012;Vrugt et al, 2008). More importantly, the BMA approach is able to produce accurate and reliable predictions due to its advantages over other multi-model techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the BMA procedure can produce the prediction probability distribution which reflects the uncertainty associated with the mean prediction (Raftery et al, 1997). Finally, the relative impacts of individual uncertainty sources and the uncertainty propagation in the modeling process can be demonstrated and quantified through a hierarchical BMA (HBMA) framework (Chitsazan and Tsai, 2015;Huang and Merwade, 2023;Liu and Merwade, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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Estimating Bayesian Model Averaging Weights and Variances of Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo

Huang

Merwade

2022

Preprint

View full text Add to dashboard Cite

As all kinds of physics-based and data-driven models are emerging in the fields of hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi-model methods used to account for the various uncertainty sources in the flood modeling process and generate robust ensemble predictions based on multiple competitive candidate models. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in the BMA parameters with fixed values, which are usually obtained from the 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, the Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in the Markov Chain Monte Carlo (MCMC) method, is proposed to estimate the BMA parameters and quantify their associated uncertainty. Both numerical experiments and the one-dimensional HEC-RAS models are employed to examine the applicability of the M-H algorithm with multiple independent Markov chains. The performances of the EM and M-H algorithms in the BMA analysis are compared based on the daily water stage predictions from 10 model configurations. The results show that the BMA weights estimated from both algorithms are comparable, while the BMA variances obtained from the M-H MCMC algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in the M-H algorithm can yield narrower distributions for the BMA weights than those from the uniform prior. Overall, the MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its prediction performance is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.

show abstract

Uncertainty Analysis and Quantification in Flood Insurance Rate Maps Using Bayesian Model Averaging and Hierarchical BMA

Cited by 9 publications

References 64 publications

Multi-View Ensemble Learning With Missing Data: Computational Framework and Evaluations using Novel Data from the Safe Autonomous Driving Domain

Multi-View Ensemble Learning With Missing Data: Computational Framework and Evaluations using Novel Data from the Safe Autonomous Driving Domain

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

Estimating Bayesian Model Averaging Weights and Variances of Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo

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