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...
| Published in: | Permafrost and Periglacial Processes |
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| Main Authors: | , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2008
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| Online Access: | 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 |
| Summary: | 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. |
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