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Recent advances in geostatistical inference on hydrogeological variables
Author(s) -
Kitanidis Peter K.
Publication year - 1995
Publication title -
reviews of geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.087
H-Index - 156
eISSN - 1944-9208
pISSN - 8755-1209
DOI - 10.1029/95rg00183
Subject(s) - hydrogeology , inference , computer science , geostatistics , causal inference , environmental science , data mining , econometrics , statistics , geology , mathematics , geotechnical engineering , artificial intelligence , spatial variability
Among the challenges facing hydrogeologists are to (a) predict where the contaminants are and where they are going and (b) to design effective remediation schemes, To meet these challenges, they must analyze data in order to estimate the parameters of geologic formations that affect the flow of water or the transport of chemicals. However, because these parameters vary in complicated and insufficiently understood ways, estimates are usually approximate and hard to obtain. Statistical methods are helpful in selecting the “best” possible estimate given the information and in evaluating the confidence bounds associated with this estimate. Furthermore, they can be used to evaluate the prediction uncertainty and are thus valuable in assessing management strategies and monitoring plans.