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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.
DOI: 10.4211/hs.4901a0d654334c259f4ff9b49dc0a74e
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Sharing Status: Published
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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.

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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

Resource Specific

Software
Programming Language Python
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/
CC-BY

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