Publikationsdatum:
2024-02-28
Beschreibung:
〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Hydrogeological information about an aquifer is difficult and costly to obtain, yet essential for the efficient management of groundwater resources. Transferring information from sampled sites to a specific site of interest can provide information when site‐specific data is lacking. Central to this approach is the notion of site similarity, which is necessary for determining relevant sites to include in the data transfer process. In this paper, we present a data‐driven method for defining site similarity. We apply this method to selecting groups of similar sites from which to derive prior distributions for the Bayesian estimation of hydraulic conductivity measurements at sites of interest. We conclude that there is now a unique opportunity to combine hydrogeological expertise with data‐driven methods to improve the predictive ability of stochastic hydrogeological models.〈/p〉
Beschreibung:
〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉〈italic〉Article impact statement〈/italic〉: This article introduces hierarchical clustering as a method for defining a notion of site similarity; the aim of this method is to improve the derivation of prior distributions in Bayesian methods in hydrogeology.〈/p〉
Beschreibung:
Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659
Beschreibung:
https://github.com/GeoStat-Bayesian/geostatDB
Beschreibung:
https://github.com/GeoStat-Bayesian/exPrior
Beschreibung:
https://github.com/GeoStat-Bayesian/siteSimilarity
Schlagwort(e):
ddc:551.49
;
hydrogeological sites
;
hydrogeological modeling
Sprache:
Englisch
Materialart:
doc-type:article
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