Computer Model Inversion and Uncertainty Quantification in the Geosciences
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Summary
The analysis suggests that model simplifications have the potential to corrupt many types of predictions, including integrated surface water/groundwater modeling, tephra fallout modeling, geophysical inversion, and hydrothermal groundwater modeling.
- Type
- article
- Published
- 2014-01-01
- Cited by
- 0
- References
- 103
- Access
- Open access
- OpenAlex
- https://openalex.org/W67869868
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:126657511
Keywords
Uncertainty quantification, Inversion (geology), Geology, Computer science, Environmental science
References
- Editor’s message: Groundwater modeling fantasies—part 2, down to earth
- Documentation of the Surface-Water Routing (SWR1) Process for modeling surface-water flow with the U.S. Geological Survey Modular Ground-Water Model (MODFLOW-2005)
- Limitations of the Advection-Diffusion Equation for Modeling Tephra Fallout: 1992 Eruption of Cerro Negro Volcano, Nicaragua
- Arc volcanism : physics and tectonics : proceedings of a 1981 IAVCEI Symposium, Arc Volcanism, August-September, 1981, Tokyo and Hakone
- Geothermal Energy: An Alternative Resource for the 21st Century
- Role of the calibration process in reducing model predictive error
- Assessment and Propagation of Model Uncertainty
- Effective Groundwater Model Calibration: With Analysis of Data, Sensitivities, Predictions, and Uncertainty
- Comment on “Pursuing the method of multiple working hypotheses for hydrological modeling” by P. Clark et al.
- Parameter estimation and hypothesis testing in linear models
- Evaluating model structure adequacy: The case of the Maggia Valley groundwater system, southern Switzerland
- Parameter estimation and inverse problems
- SEAWAT Version 4: A Computer Program for Simulation of Multi-Species Solute and Heat Transport
- Active tectonics of the Turkish‐Iranian Plateau
- Potential theory in gravity and magnetic applications
- Geophysical data analysis : discrete inverse theory
- Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation
- Conceptual model uncertainty in groundwater modeling: Combining generalized likelihood uncertainty estimation and Bayesian model averaging
- Parameter and predictive outcomes of model simplification
- Bayesian Mode Regression
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