Periglacial distribution modelling with a boosting method

Abstract We assessed the applicability of a boosting method in periglacial distribution modelling using empirically derived data on cryoturbation, sporadic permafrost and sorted solifluction from an area of 600 km 2 in sub‐Arctic Finland. The main aims were: (1) to compare the predictive ability of...

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Published in:Permafrost and Periglacial Processes
Main Authors: Hjort, Jan, Marmion, Mathieu
Other Authors: Academy of Finland
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2008
Subjects:
Online Access:http://dx.doi.org/10.1002/ppp.629
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spelling crwiley:10.1002/ppp.629 2024-06-02T08:01:27+00:00 Periglacial distribution modelling with a boosting method Hjort, Jan Marmion, Mathieu Academy of Finland 2008 http://dx.doi.org/10.1002/ppp.629 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fppp.629 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.629 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Permafrost and Periglacial Processes volume 20, issue 1, page 15-25 ISSN 1045-6740 1099-1530 journal-article 2008 crwiley https://doi.org/10.1002/ppp.629 2024-05-03T11:19:02Z Abstract We assessed the applicability of a boosting method in periglacial distribution modelling using empirically derived data on cryoturbation, sporadic permafrost and sorted solifluction from an area of 600 km 2 in sub‐Arctic Finland. The main aims were: (1) to compare the predictive ability of the generalised boosting method used with more common parametric techniques (generalised linear model) and machine‐learning methods (artificial neural networks) and (2) to assess the tenability of the explanatory variables highlighted by the generalised boosting method. The results showed the robustness of the boosting method in predicting the distribution of periglacial phenomena in the sub‐Arctic landscape. Furthermore, the environmental factors selected by the boosting method coincided well with the expected controls of the phenomena. The strengths of the generalised boosting method lie in its high predictive ability, flexibility in capturing complex process‐environment relationships and realistic model outcomes. Copyright © 2008 John Wiley & Sons, Ltd. Article in Journal/Newspaper Arctic permafrost Permafrost and Periglacial Processes Wiley Online Library Arctic Permafrost and Periglacial Processes 20 1 15 25
institution Open Polar
collection Wiley Online Library
op_collection_id crwiley
language English
description Abstract We assessed the applicability of a boosting method in periglacial distribution modelling using empirically derived data on cryoturbation, sporadic permafrost and sorted solifluction from an area of 600 km 2 in sub‐Arctic Finland. The main aims were: (1) to compare the predictive ability of the generalised boosting method used with more common parametric techniques (generalised linear model) and machine‐learning methods (artificial neural networks) and (2) to assess the tenability of the explanatory variables highlighted by the generalised boosting method. The results showed the robustness of the boosting method in predicting the distribution of periglacial phenomena in the sub‐Arctic landscape. Furthermore, the environmental factors selected by the boosting method coincided well with the expected controls of the phenomena. The strengths of the generalised boosting method lie in its high predictive ability, flexibility in capturing complex process‐environment relationships and realistic model outcomes. Copyright © 2008 John Wiley & Sons, Ltd.
author2 Academy of Finland
format Article in Journal/Newspaper
author Hjort, Jan
Marmion, Mathieu
spellingShingle Hjort, Jan
Marmion, Mathieu
Periglacial distribution modelling with a boosting method
author_facet Hjort, Jan
Marmion, Mathieu
author_sort Hjort, Jan
title Periglacial distribution modelling with a boosting method
title_short Periglacial distribution modelling with a boosting method
title_full Periglacial distribution modelling with a boosting method
title_fullStr Periglacial distribution modelling with a boosting method
title_full_unstemmed Periglacial distribution modelling with a boosting method
title_sort periglacial distribution modelling with a boosting method
publisher Wiley
publishDate 2008
url http://dx.doi.org/10.1002/ppp.629
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fppp.629
https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.629
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Permafrost and Periglacial Processes
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Permafrost and Periglacial Processes
op_source Permafrost and Periglacial Processes
volume 20, issue 1, page 15-25
ISSN 1045-6740 1099-1530
op_rights http://onlinelibrary.wiley.com/termsAndConditions#vor
op_doi https://doi.org/10.1002/ppp.629
container_title Permafrost and Periglacial Processes
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