Global sensitivity analysis on a numerical model of seawater intrusion and its implications for coastal aquifer management: a case study in Dagu River Basin, Jiaozhou Bay, China

2020 ◽  
Vol 28 (7) ◽  
pp. 2543-2557
Author(s):  
Di Zhang ◽  
Yun Yang ◽  
Jianfeng Wu ◽  
Xilai Zheng ◽  
Guanqun Liu ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Numerical Model ◽  
River Basin ◽  
Coastal Aquifer ◽  
Global Sensitivity Analysis ◽  
Jiaozhou Bay ◽  
Aquifer Management ◽  
Global Sensitivity ◽  
Coastal Aquifer Management

Current Practice and Future Challenges in Coastal Aquifer Management: Flux-Based and Trigger-Level Approaches with Application to an Australian Case Study

2011 ◽  
Vol 25 (7) ◽  
pp. 1831-1853 ◽  
Author(s):  
Adrian D. Werner ◽  
Darren W. Alcoe ◽  
Carlos M. Ordens ◽  
John L. Hutson ◽  
James D. Ward ◽  
...  
Keyword(s):  
Coastal Aquifer ◽  
Current Practice ◽  
Trigger Level ◽  
Aquifer Management ◽  
Future Challenges ◽  
Australian Case ◽  
Coastal Aquifer Management

A Methodology for Global Sensitivity Analysis of Activated Sludge Models: Case Study with Activated Sludge Model No. 3 ( ASM 3)

2019 ◽  
Vol 91 (9) ◽  
pp. 865-876
Author(s):  
Dhan Lord B. Fortela ◽  
Kyle Farmer ◽  
Alex Zappi ◽  
Wayne W. Sharp ◽  
Emmanuel Revellame ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Activated Sludge ◽  
Global Sensitivity Analysis ◽  
Activated Sludge Model ◽  
Global Sensitivity ◽  
Activated Sludge Models

Global sensitivity analysis of an urban microclimate system under uncertainty: Design and case study

2017 ◽  
Vol 124 ◽  
pp. 153-170 ◽  
Author(s):  
Jiachen Mao ◽  
Joseph H. Yang ◽  
Afshin Afshari ◽  
Leslie K. Norford
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Urban Microclimate ◽  
Global Sensitivity

scholarly journals Variable Selection in Regression Models Using Global Sensitivity Analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
William Becker ◽  
Paolo Paruolo ◽  
Andrea Saltelli
Keyword(s):  
Sensitivity Analysis ◽  
Variable Selection ◽  
Regression Models ◽  
Global Sensitivity Analysis ◽  
Physical Models ◽  
Model Fit ◽  
Global Sensitivity ◽  
Testing Sequence ◽  
Input Variables

Abstract Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis tools in the context of variable selection in regression models. Specifically, a global sensitivity measure is applied to a criterion of model fit, hence defining a ranking of regressors by importance; a testing sequence based on the ‘Pantula-principle’ is then applied to the corresponding nested submodels, obtaining a novel model-selection method. The approach is demonstrated on a growth regression case study, and on a number of simulation experiments, and it is found competitive with existing approaches to variable selection.

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