Benchmarking Knowledge-assisted Kriging for Automated Spatial Interpolation of Wind Measurements
Benchmarking Knowledge-assisted Kriging for Automated Spatial Interpolation of Wind Measurements
Abstract - We have benchmarked a novel knowledge-assisted kriging algorithm that allows regions of spatial cohesion to be specified and variograms calculated for each region. The variogram calculation itself is automated and spatial regions are created via offline automated segmentation of either expert-drawn Google Earth polygons or NASA altitude data. Our use-case is to create wind interpolation grids for input into a bathing water quality model of microbial contamination. We benchmark our knowledge-assisted kriging algorithm against 7 other algorithms on UK met-office wind data (189 sensors). Our wind estimation results are comparable, but not better than ordinary kriging, but the kriging error maps are much sharper and reflect the known spatial features better. These results are very promising when considering it is an automated approach and allows on-demand datasets to be selected and thus real-time interpolation of previously unknown measurements. Automation is important in progressing towards a pan-European interpolation service capability.
Data Fusion, Kriging, Spatial Interpolation, Wind Speed, Wind Direction, OGC, WPS
978-0-9824438-1-1
Zlatev, Zlatko
8f2e3635-d76c-46e2-85b9-53cc223fee01
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Zlatev, Zlatko
8f2e3635-d76c-46e2-85b9-53cc223fee01
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Zlatev, Zlatko, Middleton, Stuart and Veres, Galina
(2010)
Benchmarking Knowledge-assisted Kriging for Automated Spatial Interpolation of Wind Measurements.
Fusion 2010, Edinburgh.
(Submitted)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Abstract - We have benchmarked a novel knowledge-assisted kriging algorithm that allows regions of spatial cohesion to be specified and variograms calculated for each region. The variogram calculation itself is automated and spatial regions are created via offline automated segmentation of either expert-drawn Google Earth polygons or NASA altitude data. Our use-case is to create wind interpolation grids for input into a bathing water quality model of microbial contamination. We benchmark our knowledge-assisted kriging algorithm against 7 other algorithms on UK met-office wind data (189 sensors). Our wind estimation results are comparable, but not better than ordinary kriging, but the kriging error maps are much sharper and reflect the known spatial features better. These results are very promising when considering it is an automated approach and allows on-demand datasets to be selected and thus real-time interpolation of previously unknown measurements. Automation is important in progressing towards a pan-European interpolation service capability.
Text
2010_-_22319.pdf
- Other
More information
Submitted date: July 2010
Additional Information:
Event Dates: July 2010
Venue - Dates:
Fusion 2010, Edinburgh, 2010-07-01
Keywords:
Data Fusion, Kriging, Spatial Interpolation, Wind Speed, Wind Direction, OGC, WPS
Organisations:
Electronics & Computer Science, IT Innovation
Identifiers
Local EPrints ID: 272320
URI: http://eprints.soton.ac.uk/id/eprint/272320
ISBN: 978-0-9824438-1-1
PURE UUID: 96d850c8-cb38-4f21-abac-ff3a81c1d1fe
Catalogue record
Date deposited: 24 May 2011 12:48
Last modified: 15 Mar 2024 03:08
Export record
Contributors
Author:
Zlatko Zlatev
Author:
Galina Veres
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
