A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment
Global warming accelerates permafrost degradation, compromising the reliability of critical infrastructure relied upon by over five million people daily. Additionally, permafrost thaw releases substantial methane emissions due to the thawing of swamps, further amplifying global warming and climate c...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11014074/ |
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| author | Polina Pilyugina Timofey Chernikov Maria Smirnova Alexey Zaytsev Alexander Bulkin Evgeny Burnaev Ilya S. Belalov Nazar Sotiriadi Albert Efimov Yury Maximov Oleg Anisimov |
| author_facet | Polina Pilyugina Timofey Chernikov Maria Smirnova Alexey Zaytsev Alexander Bulkin Evgeny Burnaev Ilya S. Belalov Nazar Sotiriadi Albert Efimov Yury Maximov Oleg Anisimov |
| author_sort | Polina Pilyugina |
| collection | DOAJ |
| description | Global warming accelerates permafrost degradation, compromising the reliability of critical infrastructure relied upon by over five million people daily. Additionally, permafrost thaw releases substantial methane emissions due to the thawing of swamps, further amplifying global warming and climate change and thus posing a significant threat to more than eight billion people worldwide. To mitigate this growing risk, policymakers and stakeholders need accurate predictions of permafrost thaw progression. Comprehensive physics-based permafrost models often require complex, location-specific fine-tuning, making them impractical for widespread use. Although simpler models with fewer input parameters offer convenience, they generally lack accuracy. Purely data-driven models also face limitations due to the spatial and temporal sparsity of observational data. This work develops a physics-informed machine learning framework to predict permafrost thaw rates. By integrating a physics-based model into machine learning, the framework significantly enhances the feature set, enabling models to train on higher-quality data. This approach improves permafrost thaw rate predictions, supporting more reliable decision-making for construction and infrastructure maintenance in permafrost-vulnerable regions, with a forecast horizon spanning several decades. |
| format | Article |
| id | doaj-art-eff013845a904bb0bb8fd75c3c97de05 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eff013845a904bb0bb8fd75c3c97de052025-08-20T02:40:07ZengIEEEIEEE Access2169-35362025-01-0113964239643310.1109/ACCESS.2025.357307211014074A Physics-Informed Machine Learning Framework for Permafrost Stability AssessmentPolina Pilyugina0https://orcid.org/0009-0009-2838-4759Timofey Chernikov1https://orcid.org/0009-0004-8192-3751Maria Smirnova2https://orcid.org/0009-0001-2005-3367Alexey Zaytsev3https://orcid.org/0000-0002-1653-0204Alexander Bulkin4https://orcid.org/0000-0002-2052-8190Evgeny Burnaev5https://orcid.org/0000-0001-8424-0690Ilya S. Belalov6https://orcid.org/0000-0002-5447-4872Nazar Sotiriadi7https://orcid.org/0009-0006-1116-896XAlbert Efimov8https://orcid.org/0000-0001-6857-8659Yury Maximov9Oleg Anisimov10Skolkovo Institute of Science and Technology, Moscow, RussiaMoscow Institute of Physics and Technology, Moscow, RussiaMoscow Institute of Physics and Technology, Moscow, RussiaSkolkovo Institute of Science and Technology, Moscow, RussiaInternational Center for Corporate Data Analysis, Saint Martin d’Hères, FranceSkolkovo Institute of Science and Technology, Moscow, RussiaFRC Biotechnology RAS, Moscow, RussiaSberbank of Russia PJSC, Moscow, RussiaSber Innovation and Research, Sberbank of Russia, Moscow, RussiaLos Alamos National Laboratory, Los Alamos, NM, USAState Hydrological Institute, St. Petersburg, RussiaGlobal warming accelerates permafrost degradation, compromising the reliability of critical infrastructure relied upon by over five million people daily. Additionally, permafrost thaw releases substantial methane emissions due to the thawing of swamps, further amplifying global warming and climate change and thus posing a significant threat to more than eight billion people worldwide. To mitigate this growing risk, policymakers and stakeholders need accurate predictions of permafrost thaw progression. Comprehensive physics-based permafrost models often require complex, location-specific fine-tuning, making them impractical for widespread use. Although simpler models with fewer input parameters offer convenience, they generally lack accuracy. Purely data-driven models also face limitations due to the spatial and temporal sparsity of observational data. This work develops a physics-informed machine learning framework to predict permafrost thaw rates. By integrating a physics-based model into machine learning, the framework significantly enhances the feature set, enabling models to train on higher-quality data. This approach improves permafrost thaw rate predictions, supporting more reliable decision-making for construction and infrastructure maintenance in permafrost-vulnerable regions, with a forecast horizon spanning several decades.https://ieeexplore.ieee.org/document/11014074/Permafrost thawclimate changephysics-informed machine learning framework |
| spellingShingle | Polina Pilyugina Timofey Chernikov Maria Smirnova Alexey Zaytsev Alexander Bulkin Evgeny Burnaev Ilya S. Belalov Nazar Sotiriadi Albert Efimov Yury Maximov Oleg Anisimov A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment IEEE Access Permafrost thaw climate change physics-informed machine learning framework |
| title | A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment |
| title_full | A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment |
| title_fullStr | A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment |
| title_full_unstemmed | A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment |
| title_short | A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment |
| title_sort | physics informed machine learning framework for permafrost stability assessment |
| topic | Permafrost thaw climate change physics-informed machine learning framework |
| url | https://ieeexplore.ieee.org/document/11014074/ |
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