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Defining Hydrogeological Site Similarity with Hierarchical Agglomerative Clustering
Author(s) -
Kawa Nura,
Cucchi Karina,
Rubin Yoram,
Attinger Sabine,
Heße Falk
Publication year - 2022
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/gwat.13261
Subject(s) - hierarchical clustering , similarity (geometry) , cluster analysis , computer science , artificial intelligence , image (mathematics)
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.