Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

Wang, Hongrui; Wang, Cheng; Wang, Ying; Gao, Xiong; Chen, Yu

doi:10.1016/j.jhydrol.2017.03.073

Cited by 46 publications

(20 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Model uncertainty for the WSP model, with an allowable parameter range, was estimated with R-FME-based uncertainty analysis and GLUE analysis. The Bayesian–MCMC method has been widely used in uncertainty analysis [ 59 ]. Parameter uncertainty, with an allowable parameter range, was quantified with the R-FME “modMCMC” function.…”

Section: Resultsmentioning

confidence: 99%

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC

Mukhtar

Lin

Shipin

et al. 2017

IJERPH

View full text Add to dashboard Cite

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH3-N and NO3-N. Results indicate that the integrated FME-GLUE-based model, with good Nash–Sutcliffe coefficients (0.53–0.69) and correlation coefficients (0.76–0.83), successfully simulates the concentrations of ON-N, NH3-N and NO3-N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH3-N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO3-N simulation, which was measured using global sensitivity.

show abstract

Section: Resultsmentioning

confidence: 99%

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC

Mukhtar

Lin

Shipin

et al. 2017

IJERPH

View full text Add to dashboard Cite

show abstract

“…In several research fields, probabilistic methods are useful for dealing with problems related to the robustness of systems affected by uncertainties [55]. Particularly, Monte Carlo Randomized Algorithm has been used for uncertainty quantification in many applications [67][68][69]. In this paper, the Monte Carlo Randomized Algorithm is applied using the procedure described in 2.3. e number of simulations, N � 1000, is obtained with equation ( 10), adopting a confidence (δ) of 0.01 and an accuracy (ε) of 0.005. erefore, the following test demonstrates the technique success from a statistical point of view [70][71][72].…”

Section: Simulation Under Parametricmentioning

confidence: 99%

Nonlinear Control for Bioprocesses with Model Uncertainties and External Disturbances

Fernández

Pantano

Rodríguez

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, a new alternative for profiles tracking control considering additive uncertainties is proposed. Based on a previously presented work about a nonlinear and multivariable controller design for a fed-batch bioethanol production, parametric uncertainty and process disturbance are taken into account to find a more reliable control strategy for a successful industrial implementation. To decrease the uncertainties effect, an approach based on the error estimation using Newton’s backward interpolation is included in the design equations. The proposed modification assures the error convergence to zero (demonstration is shown) despite the uncertainties, which is one of the main contributions of this work. A comparison between the new, the original proposal, and another methodology is exposed.

show abstract

“…Lima et al [16] conduct flood frequency analysis with a hierarchical Bayesian framework, which estimates Generalized Extreme Value (GEV) distribution parameters in a local sense for explicitly modeling and uncertainties reduce. Recently, Wang et al [10] proposed a Bayesian-based method, which establishes a posterior distribution for daily flow rate forecasts and uncertainty quantifications.…”

Section: Data-driven Model For Floodmentioning

confidence: 99%

“…Data-driven model directly models mathematical interactions between different hydrological factors and run-off values based on historical observations. In other words, data-driven models learn mapping between flooding cues and flow rates without considering detailed physical processes, which is the main difference between physical models and data-driven models [10]. Due to rapid development of machine learning technology, many novel data-driven forecasting methods have been proposed and practiced, including Bayesian network [1], SVM model [11], neural network [6], and their variations and integrations.…”

Section: Introductionmentioning

confidence: 99%

Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution

Ding

Zhu

et al. 2020

Complexity

View full text Add to dashboard Cite

With significant development of sensors and Internet of things, researchers nowadays can easily know what happens in physical space by acquiring time-varying values of various factors. Essentially, growing data category and size greatly contribute to solve problems happened in physical space. In this paper, we aim to solve a complex problem that affects both cities and villages, i.e., flood. To reduce impacts induced by floods, hydrological factors acquired from physical space and data-driven models in cyber space have been adopted to accurately forecast floods. Considering the significance of modeling attention capability among hydrology factors, we believe extraction of discriminative hydrology factors not only reflect natural rules in physical space, but also optimally model iterations of factors to forecast run-off values in cyber space. Therefore, we propose a novel data-driven model named as STA-LSTM by integrating Long Short-Term Memory (LSTM) structure and spatiotemporal attention module, which is capable of forecasting floods for small- and medium-sized rivers. The proposed spatiotemporal attention module firstly explores spatial relationship between input hydrological factors from different locations and run-off outputs, which assigns time-varying weights to various factors. Afterwards, the proposed attention module allocates temporal-dependent weights to hidden output of each LSTM cell, which describes significance of state output for final forecasting results. Taking Lech and Changhua river basins as cases of physical space, several groups of comparative experiments show that STA-LSTM is capable to optimize complexity of mathematically modeling floods in cyber space.

show abstract

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

Cited by 46 publications

References 23 publications

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC

Nonlinear Control for Bioprocesses with Model Uncertainties and External Disturbances

Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution

Contact Info

Product

Resources

About