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A Study of Methods for Spatial Interpolation of Fire Weather in the Canadian Prairies

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dc.contributor.advisor Ali, Ayesha
dc.contributor.advisor Nadeem, Khurram
dc.contributor.author Cheng, Yue
dc.date.accessioned 2020-05-04T16:53:21Z
dc.date.available 2020-05-04T16:53:21Z
dc.date.copyright 2020-05
dc.date.created 2020-04-06
dc.date.issued 2020-05-04
dc.identifier.uri http://hdl.handle.net/10214/17899
dc.description.abstract Thousands of wildfires occur in Canadian forests every year and it is very challenging for fire management agencies to predict weather conditions and fire risk especially in the areas with low weather station density. This thesis compares several existing interpolation models (ordinary kriging, ordinary cokriging, and thin-plate spline smoothing) to the inverse distance weighting which is used by Canadian fire management agencies using weather station data from Manitoba and Saskatchewan on a daily basis. The North American Regional Reanalysis (NARR) data extracted from physical models, which has a strong correlation with weather station data, is integrated into spatial interpolation models. This thesis also integrates elevation into ordinary cokriging and the thin-plate spline smoothing models. Results show that integrating NARR into ordinary cokriging and thin-plate spline smoothing model increases prediction accuracy in areas with low station density and could be potentially useful for Canadian fire management agencies. en_US
dc.language.iso en en_US
dc.subject wildfires en_US
dc.subject spatial interpolation en_US
dc.title A Study of Methods for Spatial Interpolation of Fire Weather in the Canadian Prairies en_US
dc.type Article en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US
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