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| academic_opportunities:2021 [2021-02-08 10:39 am] – hcho | academic_opportunities:2021 [2021-03-30 08:14 am] (current) – hcho |
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| * Monday, March 22--Tuesday, March 23, 2021 | * Monday, March 22--Tuesday, March 23, 2021 |
| * Online | * Online |
| * Abstracts due by Friday, January 22 at 4pm EST | * <del>Abstracts due by Friday, January 22 at 4pm EST</del> |
| * Accepted abstract | * Accepted abstract |
| * Uncertainty estimation in hydrologic modeling using Bayesian model averaging within the GLUE framework | * Uncertainty estimation in hydrologic modeling using Bayesian model averaging within the GLUE framework |
| * Authors: Huidae Cho, Aboalhasan Fathabadi, Seyed Morteza Seyedian, Bahram Choubin | * Authors: Huidae Cho, Aboalhasan Fathabadi, Seyed Morteza Seyedian, Bahram Choubin |
| * Abstract: The generalized likelihood uncertainty estimation (GLUE) framework has widely been used for uncertainty estimation in hydrologic modeling thanks to its ease of implementation and less strict statistical assumptions about residual errors. However, its subjective factors such as likelihood functions, their threshold values for model classification, and how individual likelihood values are weighted to construct cumulative likelihood distributions play a non-significant role in uncertainty estimation. In this research, we used Bayesian model averaging (BMA), multi-objective optimization, and the k-nearest neighbor (KNN) algorithm within the GLUE framework to replace the conventional likelihood weighting method and compared their performance. We tested two likelihood functions including the Nash-Sutcliffe efficiency (NSE) and flow duration curve (FDC) to evaluate the predictive uncertainty of the Genie Rural (GR) model for the Chehelchay mountain watershed in Minodasht, Golestan province, Iran. The conventional weighting, multi-objective optimization, and KNN methods were more sensitive to the selection of a likelihood function and the FDC likelihood function produces wider predictive uncertainty bounds compared to the NSE function. In contrast, the BMA method produced predictive uncertainty bounds that are more reliable and similar for both likelihood functions, and hence was less sensitive to the selection of a likelihood function. These reliability and insensitivity of a likelihood weighting method to the likelihood function are important features in uncertainty estimation within the GLUE framework. | * Abstract: The generalized likelihood uncertainty estimation (GLUE) framework has widely been used for uncertainty estimation in hydrologic modeling thanks to its ease of implementation and less strict statistical assumptions about residual errors. However, its subjective factors such as likelihood functions, their threshold values for model classification, and how individual likelihood values are weighted to construct cumulative likelihood distributions play a non-significant role in uncertainty estimation. In this research, we used Bayesian model averaging (BMA), multi-objective optimization, and the k-nearest neighbor (KNN) algorithm within the GLUE framework to replace the conventional likelihood weighting method and compared their performance. We tested two likelihood functions including the Nash-Sutcliffe efficiency (NSE) and flow duration curve (FDC) to evaluate the predictive uncertainty of the Genie Rural (GR) model for the Chehelchay mountain watershed in Minodasht, Golestan province, Iran. The conventional weighting, multi-objective optimization, and KNN methods were more sensitive to the selection of a likelihood function and the FDC likelihood function produces wider predictive uncertainty bounds compared to the NSE function. In contrast, the BMA method produced predictive uncertainty bounds that are more reliable and similar for both likelihood functions, and hence was less sensitive to the selection of a likelihood function. These reliability and insensitivity of a likelihood weighting method to the likelihood function are important features in uncertainty estimation within the GLUE framework. |
| | * <del>Presentation recordings due by Tuesday, February 23, 2021</del> |
| |
| ===== UNG's 26th Annual Research Conference ===== | ===== UNG's 26th Annual Research Conference ===== |
| * Online | * Online |
| * Abstracts due by Friday, February 19, 2021 at midnight | * Abstracts due by Friday, February 19, 2021 at midnight |
| | |
| | ===== Google Summer of Code 2021 ===== |
| | |
| | * https://trac.osgeo.org/grass/wiki/GSoC/2021#Parallelizationofexistingmodules |
| | * https://summerofcode.withgoogle.com/ |
| | * Student application: Monday, March 29--Tuesday, April 13, 2021 |
| |
| ===== GeoPython 2021 ===== | ===== GeoPython 2021 ===== |
| * https://2021.foss4g.org/ | * https://2021.foss4g.org/ |
| * https://2021.foss4g.org/call-for-papers/call-for-papers | * https://2021.foss4g.org/call-for-papers/call-for-papers |
| | * Abstracts due by Monday, April 19, 2021 |
| * Monday, September 27--Saturday, October 2, 2021 | * Monday, September 27--Saturday, October 2, 2021 |
| * Buenos Aires, Argentina | * Online |
| * Invited to be part of the Program Committee for the General Track | * Invited to be part of the Program Committee for the General Track |
