scholarly journals Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Monika Culka
Keyword(s):  
Uncertainty Analysis ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Energy Models ◽  
Input Variables

scholarly journals Quantifying multi-source uncertainties in multi-model predictions using the Bayesian model averaging scheme

2017 ◽  
Vol 49 (3) ◽  
pp. 954-970 ◽  
Author(s):  
Shanhu Jiang ◽  
Liliang Ren ◽  
Chong-Yu Xu ◽  
Shuya Liu ◽  
Fei Yuan ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Prediction Interval ◽  
Model Averaging ◽  
Streamflow Prediction ◽  
Input Uncertainty ◽  
Shuffled Complex Evolution ◽  
Model Predictions ◽  
Combination Approach

Abstract This study focuses on a quantitative multi-source uncertainty analysis of multi-model predictions. Three widely used hydrological models, i.e., Xinanjiang (XAJ), hybrid rainfall–runoff (HYB), and HYMOD (HYM), were calibrated by two parameter optimization algorithms, namely, shuffled complex evolution (SCE-UA) method and shuffled complex evolution metropolis (SCEM-UA) method on the Mishui basin, south China. The input uncertainty was quantified by utilizing a normally distributed error multiplier. The ensemble simulation sets calculated from the three models were combined using the Bayesian model averaging (BMA) method. Results indicate the following. (1) Both SCE-UA and SCEM-UA resulted in good and comparable streamflow simulations. Specifically, the SCEM-UA implied parameter uncertainty and provided the posterior distribution of the parameters. (2) In terms of the precipitation input uncertainty, precision of streamflow simulations did not improve remarkably. (3) The BMA combination not only improved the precision of streamflow prediction, but also quantified the uncertainty bounds of the simulation. (4) The prediction interval calculated using the SCEM-UA-based BMA combination approach appears superior to that calculated using the SCE-UA-based BMA combination for both high flows and low flows. Results suggest that the comprehensive uncertainty analysis by using the SCEM-UA algorithm and BMA method is superior for streamflow predictions and flood forecasting.

Improving Streamflow Prediction Using Uncertainty Analysis and Bayesian Model Averaging

2018 ◽  
Vol 23 (5) ◽  
pp. 05018004 ◽  
Author(s):  
Antonio A. Meira Neto ◽  
Paulo Tarso S. Oliveira ◽  
Dulce B. B. Rodrigues ◽  
Edson Wendland
Keyword(s):  
Uncertainty Analysis ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Streamflow Prediction

scholarly journals Real-time flood forecasting coupling different postprocessing techniques of precipitation forecast ensembles with a distributed hydrological model. The case study of may 2008 flood in western Piemonte, Italy

2013 ◽  
Vol 13 (2) ◽  
pp. 211-220 ◽  
Author(s):  
D. Cane ◽  
S. Ghigo ◽  
D. Rabuffetti ◽  
M. Milelli
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Hydrological Model ◽  
Good Description ◽  
Model Averaging ◽  
Region Of Interest ◽  
Precipitation Forecast ◽  
Distributed Hydrological Model ◽  
Probabilistic Forecasts

Abstract. In this work, we compare the performance of an hydrological model when driven by probabilistic rain forecast derived from two different post-processing techniques. The region of interest is Piemonte, northwestern Italy, a complex orography area close to the Mediterranean Sea where the forecast are often a challenge for weather models. The May 2008 flood is here used as a case study, and the very dense weather station network allows us for a very good description of the event and initialization of the hydrological model. The ensemble probabilistic forecasts of the rainfall fields are obtained with the Bayesian model averaging, with the classical poor man ensemble approach and with a new technique, the Multimodel SuperEnsemble Dressing. In this case study, the meteo-hydrological chain initialized with the Multimodel SuperEnsemble Dressing is able to provide more valuable discharge ranges with respect to the one initialized with Bayesian model averaging multi-model.

scholarly journals Combining Grey Relational Analysis and a Bayesian Model Averaging Method to Derive Monthly Optimal Operating Rules for a Hydropower Reservoir

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1099 ◽  
Author(s):  
Guohua Fang ◽  
Yuxue Guo ◽  
Xianfeng Huang ◽  
Martine Rutten ◽  
Yu Yuan
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Hydropower Generation ◽  
Optimal Operating ◽  
Relational Analysis ◽  
Operating Rules ◽  
Input Variables ◽  
Grey Relational ◽  
Hydropower Reservoir

Various regression models are currently applied to derive functional forms of operating rules for hydropower reservoirs. It is necessary to analyze and evaluate the model selecting uncertainty involved in reservoir operating rules for efficient hydropower generation. Moreover, selecting the optimal input variables from a large number of candidates to characterize an output variable can lead to a more accurate operation simulation. Therefore, this paper combined the Grey Relational Analysis (GRA) method and the Bayesian Model Averaging (BMA) method to select input variables and derive the monthly optimal operating rules for a hydropower reservoir. The monthly input variables were first filtered according to the relationship between the preselected output and input variables based on the reservoir optimal deterministic trajectory using GRA. Three models, Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM), Adaptive Neural Fuzzy Inference System (ANFIS), and Multiple Linear Regression Analysis (MLRA) model, were further implemented to derive individual monthly operating rules. BMA was applied to determine the final monthly operating rules by analyzing the uncertainty of selecting individual models with different weights. A case study of Xinanjiang Reservoir in China shows that the combination of the two methods can achieve high-efficiency hydropower generation and optimal utilization of water resources.

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