Predicting Glacier Terminus Retreat Using Machine Learning
While a majority of mass loss from the Greenland Ice Shelf is attributed to glacial terminus retreat via calving, the superimposed force factors of the ice-ocean interface create a challenge for physically modeling terminus change. Here we use time series of environmental and glacial data, input as...
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ftzenodo:oai:zenodo.org:7442566 2023-08-15T12:41:22+02:00 Predicting Glacier Terminus Retreat Using Machine Learning Shionalyn, Kevin Catania, Ginny Trugman, Daniel Felikson, Denis Stearns, Leigh Wood, Michael 2022-12-15 https://zenodo.org/record/7442566 https://doi.org/10.5281/zenodo.7442566 eng eng doi:10.5281/zenodo.7442565 https://zenodo.org/communities/cryoai https://zenodo.org/record/7442566 https://doi.org/10.5281/zenodo.7442566 oai:zenodo.org:7442566 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/conferencePoster poster 2022 ftzenodo https://doi.org/10.5281/zenodo.744256610.5281/zenodo.7442565 2023-07-25T23:05:09Z While a majority of mass loss from the Greenland Ice Shelf is attributed to glacial terminus retreat via calving, the superimposed force factors of the ice-ocean interface create a challenge for physically modeling terminus change. Here we use time series of environmental and glacial data, input as features into a machine learning regression model, to forecast terminus retreat for marine-terminating glaciers in Greenland. We then identify the critical features that most impact a glacier’s likelihood of retreat using SHAP analysis. We further analyze the heterogeneous outcomes for individual glaciers to classify them by their terminus change profile. By better understanding the parameters impacting glacial retreat, we inform physical models to reduce uncertainty in mass change projections. Conference Object glacier Greenland Ice Shelf Zenodo Greenland |
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ftzenodo |
language |
English |
description |
While a majority of mass loss from the Greenland Ice Shelf is attributed to glacial terminus retreat via calving, the superimposed force factors of the ice-ocean interface create a challenge for physically modeling terminus change. Here we use time series of environmental and glacial data, input as features into a machine learning regression model, to forecast terminus retreat for marine-terminating glaciers in Greenland. We then identify the critical features that most impact a glacier’s likelihood of retreat using SHAP analysis. We further analyze the heterogeneous outcomes for individual glaciers to classify them by their terminus change profile. By better understanding the parameters impacting glacial retreat, we inform physical models to reduce uncertainty in mass change projections. |
format |
Conference Object |
author |
Shionalyn, Kevin Catania, Ginny Trugman, Daniel Felikson, Denis Stearns, Leigh Wood, Michael |
spellingShingle |
Shionalyn, Kevin Catania, Ginny Trugman, Daniel Felikson, Denis Stearns, Leigh Wood, Michael Predicting Glacier Terminus Retreat Using Machine Learning |
author_facet |
Shionalyn, Kevin Catania, Ginny Trugman, Daniel Felikson, Denis Stearns, Leigh Wood, Michael |
author_sort |
Shionalyn, Kevin |
title |
Predicting Glacier Terminus Retreat Using Machine Learning |
title_short |
Predicting Glacier Terminus Retreat Using Machine Learning |
title_full |
Predicting Glacier Terminus Retreat Using Machine Learning |
title_fullStr |
Predicting Glacier Terminus Retreat Using Machine Learning |
title_full_unstemmed |
Predicting Glacier Terminus Retreat Using Machine Learning |
title_sort |
predicting glacier terminus retreat using machine learning |
publishDate |
2022 |
url |
https://zenodo.org/record/7442566 https://doi.org/10.5281/zenodo.7442566 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
glacier Greenland Ice Shelf |
genre_facet |
glacier Greenland Ice Shelf |
op_relation |
doi:10.5281/zenodo.7442565 https://zenodo.org/communities/cryoai https://zenodo.org/record/7442566 https://doi.org/10.5281/zenodo.7442566 oai:zenodo.org:7442566 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.744256610.5281/zenodo.7442565 |
_version_ |
1774294632245493760 |