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| academic_opportunities:2021 [2021-02-08 10:39 am] – hcho | academic_opportunities:2021 [2024-07-21 09:03 am] (current) – ↷ Links adapted because of a move operation hcho |
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| * Submitted abstract | * Submitted abstract |
| * Modification of the Universal Soil Loss Equation Model with Climate-Change Induced Parameters and Process Automation | * Modification of the Universal Soil Loss Equation Model with Climate-Change Induced Parameters and Process Automation |
| * Authors: [[https://ung.edu/institute-environmental-spatial-analysis/faculty-staff-bio/sudhanshu-panda.php|Sudhanshu Panda]], [[:Owen Smith]], [[:Huidae Cho]], [[https://www.srs.fs.usda.gov/staff/242|Johnny M. Grace III]], [[https://www.srs.fs.usda.gov/staff/743|Devendra M. Amatya]], [[https://www.srs.fs.usda.gov/staff/scientists/834|Peter V. Caldwell]] | * Authors: [[https://ung.edu/institute-environmental-spatial-analysis/faculty-staff-bio/sudhanshu-panda.php|Sudhanshu Panda]], [[lab_members:owen_smith]], [[lab_members:huidae_cho]], [[https://www.srs.fs.usda.gov/staff/242|Johnny M. Grace III]], [[https://www.srs.fs.usda.gov/staff/743|Devendra M. Amatya]], [[https://www.srs.fs.usda.gov/staff/scientists/834|Peter V. Caldwell]] |
| * Cancelled because of COVID-19 | * Cancelled because of COVID-19 |
| |
| * Accepted abstract | * Accepted abstract |
| * r.accumulate: Efficient computation of hydrologic parameters in GRASS---Improving the performance of geospatial computation for web-based hydrologic modeling | * r.accumulate: Efficient computation of hydrologic parameters in GRASS---Improving the performance of geospatial computation for web-based hydrologic modeling |
| * Author: [[:Huidae Cho]] | * Author: [[lab_members:huidae_cho]] |
| * Abstract: The longest flow path is one of the most important geospatial parameters that is used for hydrologic analysis and modeling. However, there are not many available GIS tools that can compute this watershed parameter. At the same time, there have been almost little to no efforts in improving its computational efficiency since its first, to the presenter's best knowledge, introduction by Smith (1995) when the geospatial data resolution was relatively coarser. In this talk, the presenter introduces a new algorithm that applies Hack's law to the discovery of the longest flow path and its efficient implementation as a GRASS module called r.accumulate. He compares its performance to that of commercial ArcHydro's Longest Flow Path tool. Lastly, he introduces a proof-of-concept version of the Web-based Hydrologic Modeling System (WHydroMod) built using GRASS, PyWPS, MapServer, and OpenLayers, and discusses how r.accumulate can be used to improve the efficiency of geospatial computation for WHydroMod. | * Abstract: The longest flow path is one of the most important geospatial parameters that is used for hydrologic analysis and modeling. However, there are not many available GIS tools that can compute this watershed parameter. At the same time, there have been almost little to no efforts in improving its computational efficiency since its first, to the presenter's best knowledge, introduction by Smith (1995) when the geospatial data resolution was relatively coarser. In this talk, the presenter introduces a new algorithm that applies Hack's law to the discovery of the longest flow path and its efficient implementation as a GRASS module called r.accumulate. He compares its performance to that of commercial ArcHydro's Longest Flow Path tool. Lastly, he introduces a proof-of-concept version of the Web-based Hydrologic Modeling System (WHydroMod) built using GRASS, PyWPS, MapServer, and OpenLayers, and discusses how r.accumulate can be used to improve the efficiency of geospatial computation for WHydroMod. |
| * Start time: Sunday, February 7, 2021 at 2:40pm CET (same day at 8:40am EST) | * Start time: Sunday, February 7, 2021 at 2:40pm CET (same day at 8:40am EST) |
| * 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 |
