Discrete wavelet transform coupled with the active subspace method
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| Resource type: | Model Program Resource | |
| Storage: | The size of this resource is 11.8 MB | |
| Created: | Aug 05, 2020 at 5:12 a.m. | |
| Last updated: | Aug 05, 2020 at 5:39 a.m.
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| DOI: | 10.4211/hs.4901a0d654334c259f4ff9b49dc0a74e | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 136 |
| Downloads: | 6 |
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Abstract
The provided Python code represents the coupled framework between the discrete wavelet transform and the active subspace method. It has the goal to perform temporal scale dependent model parameter sensitivity analysis. In the provided case, the methodology is coupled to an R code containing the LuKARS model.
Subject Keywords
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Resource Level Coverage
Spatial
Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Longitude
14.7661°
Latitude
47.9208°
Content
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Resource Specific
| Programming Language | Python |
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| Operating System | Windows |
References
Sources
| Derived From: | Teixeira Parente, M., Bittner, D., Mattis, S. A., Chiogna, G., & Wohlmuth, B. (2019). Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces. Water Resources Research, 55(8), 7086-7107. |
How to Cite
| Bittner, D., M. Engel, B. Wohlmuth, D. Labat, G. Chiogna (2020). Discrete wavelet transform coupled with the active subspace method, HydroShare, https://doi.org/10.4211/hs.4901a0d654334c259f4ff9b49dc0a74e |
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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