Show simple item record

dc.contributor.authorSpicer, Rawser W.
dc.date.accessioned2021-03-02T21:55:40Z
dc.date.available2021-03-02T21:55:40Z
dc.date.issued2020-05
dc.identifier.urihttp://hdl.handle.net/11122/11879
dc.descriptionMaster's Project (M.S.) University of Alaska Fairbanks, 2020en_US
dc.description.abstractThis project examines thermokarst initiation through the application of random forest models. Thermokarst initiation marks the start of the formation of thermokarst features. Changes in landscape, due to the thermokarst process, can result in changes in wildlife habitat, as well as energy, carbon and water fluxes. Random forests are an ensemble learning technique that combines the results of many independent decision trees to create results that avoid the overfitting in regular decision trees. Random forests were trained against an existing thermokarst initiation model. Results showed that random forests were useful in this context. Random forest hyperparameters were also examined through a multiparameter sensitivity analysis.en_US
dc.language.isoen_USen_US
dc.titleExamining thermokarst initiation with random forest modelsen_US
dc.typeThesisen_US
dc.type.degreemsen_US
dc.identifier.departmentDepartment of Computer Scienceen_US
dc.contributor.chairBolton, W. Robert
dc.contributor.chairLawlor, Orion
dc.contributor.committeeChappell, Glenn
refterms.dateFOA2021-03-02T21:55:41Z


Files in this item

Thumbnail
Name:
Spicer_R_2020.pdf
Size:
13.66Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record