Numerical avalanche prediction: Kootenay Pass, British Columbia, Canada
Abstract A numerical avalanche-prediction scheme was developed for highway applications at Kootenay Pass, British Columbia. The model features parametric discriminant analysis using Bayesian statistics to predict avalanche occurrences. Cluster techniques are then employed in discriminant space to an...
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Language: | English |
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Cambridge University Press (CUP)
1994
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Online Access: | http://dx.doi.org/10.1017/s0022143000007437 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143000007437 |
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crcambridgeupr:10.1017/s0022143000007437 2024-03-03T08:46:07+00:00 Numerical avalanche prediction: Kootenay Pass, British Columbia, Canada Mcclung, D. M. Tweedy, John 1994 http://dx.doi.org/10.1017/s0022143000007437 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143000007437 en eng Cambridge University Press (CUP) Journal of Glaciology volume 40, issue 135, page 350-358 ISSN 0022-1430 1727-5652 Earth-Surface Processes journal-article 1994 crcambridgeupr https://doi.org/10.1017/s0022143000007437 2024-02-08T08:41:37Z Abstract A numerical avalanche-prediction scheme was developed for highway applications at Kootenay Pass, British Columbia. The model features parametric discriminant analysis using Bayesian statistics to predict avalanche occurrences. Cluster techniques are then employed in discriminant space to analyze avalanche occurrences by the method of nearest neighbours. Extensive numerical testing of the model using an historical data base indicates that prediction accuracy may be 70% or better for both avalanche and non-avalanche time intervals. Article in Journal/Newspaper Journal of Glaciology Cambridge University Press Canada British Columbia ENVELOPE(-125.003,-125.003,54.000,54.000) Journal of Glaciology 40 135 350 358 |
institution |
Open Polar |
collection |
Cambridge University Press |
op_collection_id |
crcambridgeupr |
language |
English |
topic |
Earth-Surface Processes |
spellingShingle |
Earth-Surface Processes Mcclung, D. M. Tweedy, John Numerical avalanche prediction: Kootenay Pass, British Columbia, Canada |
topic_facet |
Earth-Surface Processes |
description |
Abstract A numerical avalanche-prediction scheme was developed for highway applications at Kootenay Pass, British Columbia. The model features parametric discriminant analysis using Bayesian statistics to predict avalanche occurrences. Cluster techniques are then employed in discriminant space to analyze avalanche occurrences by the method of nearest neighbours. Extensive numerical testing of the model using an historical data base indicates that prediction accuracy may be 70% or better for both avalanche and non-avalanche time intervals. |
format |
Article in Journal/Newspaper |
author |
Mcclung, D. M. Tweedy, John |
author_facet |
Mcclung, D. M. Tweedy, John |
author_sort |
Mcclung, D. M. |
title |
Numerical avalanche prediction: Kootenay Pass, British Columbia, Canada |
title_short |
Numerical avalanche prediction: Kootenay Pass, British Columbia, Canada |
title_full |
Numerical avalanche prediction: Kootenay Pass, British Columbia, Canada |
title_fullStr |
Numerical avalanche prediction: Kootenay Pass, British Columbia, Canada |
title_full_unstemmed |
Numerical avalanche prediction: Kootenay Pass, British Columbia, Canada |
title_sort |
numerical avalanche prediction: kootenay pass, british columbia, canada |
publisher |
Cambridge University Press (CUP) |
publishDate |
1994 |
url |
http://dx.doi.org/10.1017/s0022143000007437 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143000007437 |
long_lat |
ENVELOPE(-125.003,-125.003,54.000,54.000) |
geographic |
Canada British Columbia |
geographic_facet |
Canada British Columbia |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Journal of Glaciology volume 40, issue 135, page 350-358 ISSN 0022-1430 1727-5652 |
op_doi |
https://doi.org/10.1017/s0022143000007437 |
container_title |
Journal of Glaciology |
container_volume |
40 |
container_issue |
135 |
container_start_page |
350 |
op_container_end_page |
358 |
_version_ |
1792502032707354624 |