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Tying geophysics to hydrogeology: a learning machine approach to characterise heterogeneous granular aquifers

A learning machine approach is proposed to define site-specific hydro-geophysical relationships in order to predict granular aquifer hydraulic properties from geophysical measurements. The learning machine is trained on a representative data set of hydraulic and geophysical measurements.
The main algorithms used for training are semisupervised fuzzy clustering and relevant vector machines (RVM) for classification and regression. This approach, which extends the capabilities of geophysical methods, represents an efficient alternative to conventional granular aquifer characterization
mainly based on hydraulic methods.

Status
In progress
Type
Project
Project URL
http://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=289410
Start Date
End Date

The Great Lakes - St. Lawrence Research Inventory is an
interactive, Internet-based, searchable database created as a tool to collect and disseminate
up-to-date information about research projects in the
Great Lakes - St. Lawrence Region.