Periglacial distribution modelling with a boosting method
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 km2 in sub‐Arctic Finland. The main aims were: (1) to compare the predictive ability of the genera...
Published in: | Permafrost and Periglacial Processes |
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Online Access: | https://doi.org/10.1002/ppp.629 |
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ftrepec:oai:RePEc:wly:perpro:v:20:y:2009:i:1:p:15-25 2023-05-15T14:54:05+02:00 Periglacial distribution modelling with a boosting method Jan Hjort Mathieu Marmion https://doi.org/10.1002/ppp.629 unknown https://doi.org/10.1002/ppp.629 article ftrepec https://doi.org/10.1002/ppp.629 2020-12-04T13:31:25Z 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 km2 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 RePEc (Research Papers in Economics) Arctic Permafrost and Periglacial Processes 20 1 15 25 |
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Open Polar |
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RePEc (Research Papers in Economics) |
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ftrepec |
language |
unknown |
description |
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 km2 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. |
format |
Article in Journal/Newspaper |
author |
Jan Hjort Mathieu Marmion |
spellingShingle |
Jan Hjort Mathieu Marmion Periglacial distribution modelling with a boosting method |
author_facet |
Jan Hjort Mathieu Marmion |
author_sort |
Jan Hjort |
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 |
url |
https://doi.org/10.1002/ppp.629 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic permafrost |
genre_facet |
Arctic permafrost |
op_relation |
https://doi.org/10.1002/ppp.629 |
op_doi |
https://doi.org/10.1002/ppp.629 |
container_title |
Permafrost and Periglacial Processes |
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20 |
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1 |
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15 |
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25 |
_version_ |
1766325776360144896 |