Susceptibility mapping of groundwater salinity using machine learning models
Susceptibility mapping of groundwater salinity using machine learning models
About this item
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
Increasing groundwater salinity has recently raised severe environmental and health concerns around the world. Advancement of the novel methods for spatial salinity modeling and prediction would be essential for effective management of the resources and planning mitigation policies. The current research presents the application of machine learning...
Alternative Titles
Full title
Susceptibility mapping of groundwater salinity using machine learning models
Authors, Artists and Contributors
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Record Identifier
TN_cdi_proquest_miscellaneous_2574348713
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2574348713
Other Identifiers
ISSN
0944-1344
E-ISSN
1614-7499
DOI
10.1007/s11356-020-11319-5
