A Methodology for Sensitivity Analysis Based on Regression: Applications to Handle Uncertainty in Natural Resources Characterization
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| 024 | 7 | 0 | |a 10.1007/s11053-014-9241-0 |2 doi |
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| 245 | 0 | 2 | |a A Methodology for Sensitivity Analysis Based on Regression: Applications to Handle Uncertainty in Natural Resources Characterization |h [Elektronische Daten] |c [Yevgeniy Zagayevskiy, Clayton Deutsch] |
| 520 | 3 | |a Uncertainty in a natural resource model represents risk that should be minimized for improved management. Natural resources are components of complex earth science systems, which can be exhaustively sampled, numerically modeled with Monte Carlo simulation, or both, to understand their underlying nature. A numerical model represents relationships between a response variable and predictor variables. Uncertainty in a response variable can be observed directly, but understanding the importance of each predictor variable requires further post-processing of the numerical model. A methodology of local sensitivity analysis, based on linear and quadratic regression models, is developed to help understand the uncertainty contribution of each predictor variable to the response model. Sensitivity coefficients, predicted response values, and summary statistics with model utility tests for the regression models are evaluated. The importance of standardized sensitivity coefficients and other measures are developed. Standardized sensitivity coefficients represent the contribution of uncertainty in the predictor variables to uncertainty in model response. Results of the sensitivity analysis are visually summarized in the form of extended tornado charts. The proposed methodology is applied to representative petroleum and mining case studies. The methodology is robust, efficient, descriptive, and straightforward. Understanding of the contribution of each predictor variable to the response variable is useful for minimization of model response uncertainty, decision-making, and further study. | |
| 540 | |a International Association for Mathematical Geosciences, 2014 | ||
| 690 | 7 | |a Local sensitivity analysis |2 nationallicence | |
| 690 | 7 | |a Sensitivity coefficients |2 nationallicence | |
| 690 | 7 | |a Tornado chart |2 nationallicence | |
| 690 | 7 | |a Linear regression model |2 nationallicence | |
| 690 | 7 | |a Quadratic regression model |2 nationallicence | |
| 700 | 1 | |a Zagayevskiy |D Yevgeniy |u Centre for Computational Geostatistics (CCG), School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, Markin/CNRL Natural Resources Engineering Facility, University of Alberta, 9105 116th St., T6G 2W2, Edmonton, AB, Canada |4 aut | |
| 700 | 1 | |a Deutsch |D Clayton |u Centre for Computational Geostatistics (CCG), School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, Markin/CNRL Natural Resources Engineering Facility, University of Alberta, 9105 116th St., T6G 2W2, Edmonton, AB, Canada |4 aut | |
| 773 | 0 | |t Natural Resources Research |d Springer US; http://www.springer-ny.com |g 24/3(2015-09-01), 239-274 |x 1520-7439 |q 24:3<239 |1 2015 |2 24 |o 11053 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11053-014-9241-0 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s11053-014-9241-0 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zagayevskiy |D Yevgeniy |u Centre for Computational Geostatistics (CCG), School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, Markin/CNRL Natural Resources Engineering Facility, University of Alberta, 9105 116th St., T6G 2W2, Edmonton, AB, Canada |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Deutsch |D Clayton |u Centre for Computational Geostatistics (CCG), School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, Markin/CNRL Natural Resources Engineering Facility, University of Alberta, 9105 116th St., T6G 2W2, Edmonton, AB, Canada |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Natural Resources Research |d Springer US; http://www.springer-ny.com |g 24/3(2015-09-01), 239-274 |x 1520-7439 |q 24:3<239 |1 2015 |2 24 |o 11053 | ||