Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method
Since Bayesian Model Averaging (BMA) method can combine the forecasts of different models together to generate a new one which is expected to be better than any individual model’s forecast, it has been widely used in hydrology for ensemble hydrologic prediction. Previous studies of the BMA mostly fo...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/346045 |
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| _version_ | 1832553628935651328 |
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| author | Leihua Dong Lihua Xiong Kun-xia Yu |
| author_facet | Leihua Dong Lihua Xiong Kun-xia Yu |
| author_sort | Leihua Dong |
| collection | DOAJ |
| description | Since Bayesian Model Averaging (BMA) method can combine the forecasts of different models together to generate a new one which is expected to be better than any individual model’s forecast, it has been widely used in hydrology for ensemble hydrologic prediction. Previous studies of the BMA mostly focused on the comparison of the BMA mean prediction with each individual model’s prediction. As BMA has the ability to provide a statistical distribution of the quantity to be forecasted, the research focus in this study is shifted onto the comparison of the prediction uncertainty interval generated by BMA with that of each individual model under two different BMA combination schemes. In the first BMA scheme, three models under the same Nash-Sutcliffe efficiency objective function are, respectively, calibrated, thus providing three-member predictions ensemble for the BMA combination. In the second BMA scheme, all three models are, respectively, calibrated under three different objective functions other than Nash-Sutcliffe efficiency to obtain nine-member predictions ensemble. Finally, the model efficiency and the uncertainty intervals of each individual model and two BMA combination schemes are assessed and compared. |
| format | Article |
| id | doaj-art-b5765fc66ab14b4a92f0c3bd63801457 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-b5765fc66ab14b4a92f0c3bd638014572025-02-03T05:53:39ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/346045346045Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging MethodLeihua Dong0Lihua Xiong1Kun-xia Yu2State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaSince Bayesian Model Averaging (BMA) method can combine the forecasts of different models together to generate a new one which is expected to be better than any individual model’s forecast, it has been widely used in hydrology for ensemble hydrologic prediction. Previous studies of the BMA mostly focused on the comparison of the BMA mean prediction with each individual model’s prediction. As BMA has the ability to provide a statistical distribution of the quantity to be forecasted, the research focus in this study is shifted onto the comparison of the prediction uncertainty interval generated by BMA with that of each individual model under two different BMA combination schemes. In the first BMA scheme, three models under the same Nash-Sutcliffe efficiency objective function are, respectively, calibrated, thus providing three-member predictions ensemble for the BMA combination. In the second BMA scheme, all three models are, respectively, calibrated under three different objective functions other than Nash-Sutcliffe efficiency to obtain nine-member predictions ensemble. Finally, the model efficiency and the uncertainty intervals of each individual model and two BMA combination schemes are assessed and compared.http://dx.doi.org/10.1155/2013/346045 |
| spellingShingle | Leihua Dong Lihua Xiong Kun-xia Yu Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method Journal of Applied Mathematics |
| title | Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method |
| title_full | Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method |
| title_fullStr | Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method |
| title_full_unstemmed | Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method |
| title_short | Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method |
| title_sort | uncertainty analysis of multiple hydrologic models using the bayesian model averaging method |
| url | http://dx.doi.org/10.1155/2013/346045 |
| work_keys_str_mv | AT leihuadong uncertaintyanalysisofmultiplehydrologicmodelsusingthebayesianmodelaveragingmethod AT lihuaxiong uncertaintyanalysisofmultiplehydrologicmodelsusingthebayesianmodelaveragingmethod AT kunxiayu uncertaintyanalysisofmultiplehydrologicmodelsusingthebayesianmodelaveragingmethod |