Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine Learning
Freezing and thawing (FT) processes in the soil active layer play a critical role in high-latitude ecosystems, influencing carbon cycling, hydrology, and infrastructure stability. Accurate prediction of FT states is essential for assessing permafrost dynamics under a changing climate. This study int...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002237 |
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| author | A. Ahajjam M. Soaper R. Chance J. Chandler T. Pasch |
| author_facet | A. Ahajjam M. Soaper R. Chance J. Chandler T. Pasch |
| author_sort | A. Ahajjam |
| collection | DOAJ |
| description | Freezing and thawing (FT) processes in the soil active layer play a critical role in high-latitude ecosystems, influencing carbon cycling, hydrology, and infrastructure stability. Accurate prediction of FT states is essential for assessing permafrost dynamics under a changing climate. This study introduces a comprehensive framework for FT state prediction that leverages MERRA-2 reanalysis climate data, ensemble machine learning, and in-situ soil temperature measurements. In addition, Genetic algorithms are employed to identify the most influential geospatial features affecting FT transitions across three shallow active layer depths. The framework is validated in two distinct regions on Alaska’s North Slope (Deadhorse and Toolik Lake) over four prediction horizons (+0, +7, +30, and +90 days). Furthermore, the study evaluates the impact of four training approaches (location-specific, cross-location, location-agnostic, and depth-agnostic) on model performance, addressing the challenge of using prediction methods in real-world scenarios. |
| format | Article |
| id | doaj-art-8f41b9a0f7ca4bad9d979485951c452d |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-8f41b9a0f7ca4bad9d979485951c452d2025-08-20T02:34:31ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-06-0114010457610.1016/j.jag.2025.104576Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine LearningA. Ahajjam0M. Soaper1R. Chance2J. Chandler3T. Pasch4School of Electrical Engineering and Computer Science, University of North Dakota, Upson Hall I, Grand Forks, 58202-7165, ND, USA; Corresponding author.Harold Hamm School of Geology and Geologic Engineering, University of North Dakota, Leonard Hall, Grand Forks, 58202-8358, ND, USAHarold Hamm School of Geology and Geologic Engineering, University of North Dakota, Leonard Hall, Grand Forks, 58202-8358, ND, USADepartment of Communication, University of North Dakota, O’Kelly Hall, Grand Forks, 58202, ND, USADepartment of Communication, University of North Dakota, O’Kelly Hall, Grand Forks, 58202, ND, USAFreezing and thawing (FT) processes in the soil active layer play a critical role in high-latitude ecosystems, influencing carbon cycling, hydrology, and infrastructure stability. Accurate prediction of FT states is essential for assessing permafrost dynamics under a changing climate. This study introduces a comprehensive framework for FT state prediction that leverages MERRA-2 reanalysis climate data, ensemble machine learning, and in-situ soil temperature measurements. In addition, Genetic algorithms are employed to identify the most influential geospatial features affecting FT transitions across three shallow active layer depths. The framework is validated in two distinct regions on Alaska’s North Slope (Deadhorse and Toolik Lake) over four prediction horizons (+0, +7, +30, and +90 days). Furthermore, the study evaluates the impact of four training approaches (location-specific, cross-location, location-agnostic, and depth-agnostic) on model performance, addressing the challenge of using prediction methods in real-world scenarios.http://www.sciencedirect.com/science/article/pii/S1569843225002237Freeze-thaw cycleSoil temperatureMERRA-2Machine learningAlaskaArctic |
| spellingShingle | A. Ahajjam M. Soaper R. Chance J. Chandler T. Pasch Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine Learning International Journal of Applied Earth Observations and Geoinformation Freeze-thaw cycle Soil temperature MERRA-2 Machine learning Alaska Arctic |
| title | Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine Learning |
| title_full | Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine Learning |
| title_fullStr | Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine Learning |
| title_full_unstemmed | Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine Learning |
| title_short | Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine Learning |
| title_sort | predicting freeze thaw states in alaska permafrost landscapes using climate reanalysis and machine learning |
| topic | Freeze-thaw cycle Soil temperature MERRA-2 Machine learning Alaska Arctic |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002237 |
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