Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model
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Abstract
For decision support, the insights and predictive power of numerical process models can be hampered by insufficient expertise and computational resources required to evaluate system response to new stresses. An alternative is to emulate the process model with a statistical “metamodel.” Built on a dataset of collocated numerical model input and output, a groundwater flow model was emulated using a Bayesian Network, an Artificial neural network, and a Gradient Boosted Regression Tree. The response of interest was surface water depletion expressed as the source of water-to-wells. The results have application for managing allocation of groundwater. Each technique was tuned using cross validation and further evaluated using a held-out dataset. A numerical MODFLOW-USG model of the Lake Michigan Basin, USA, was used for the evaluation. The performance and interpretability of each technique was compared pointing to advantages of each technique. The metamodel can extend to unmodeled areas.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model |
| Series title | Environmental Modelling and Software |
| DOI | 10.1016/j.envsoft.2015.11.023 |
| Volume | 77 |
| Year Published | 2016 |
| Language | English |
| Publisher | Elsevier |
| Publisher location | Oxford |
| Contributing office(s) | Wisconsin Water Science Center |
| Description | 13 p. |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Environmental Modelling & Software |
| First page | 95 |
| Last page | 107 |
| Country | United States |
| State | Illinois, Indiana, Michigan, Ohio, Wisconsin |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |