Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal
Permafrost is one of the key components of the cryosphere. Previous studies show that the extent of permafrost has shifted to higher elevations in Nepal. These researches, however, has been hampered by inconsistency in their study period. Proxies like rock glaciers and climatic variables, such as mu...
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KeAi Communications Co., Ltd.
2025-05-01
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| Series: | Geodesy and Geodynamics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674984724001010 |
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| author | Arnab Singh Dibas Shrestha Kaman Ghimire Sangya Mishra Darwin Rana Sunil Acharya |
| author_facet | Arnab Singh Dibas Shrestha Kaman Ghimire Sangya Mishra Darwin Rana Sunil Acharya |
| author_sort | Arnab Singh |
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| description | Permafrost is one of the key components of the cryosphere. Previous studies show that the extent of permafrost has shifted to higher elevations in Nepal. These researches, however, has been hampered by inconsistency in their study period. Proxies like rock glaciers and climatic variables, such as multi-decadal annual air temperature, are used to link towards the likely occurrence of permafrost. Here, the rock glacier inventory of Solukhumbu was prepared, and classified based on their activity (Intact/Relict) from Google Earth. Talus-based rock glaciers were observed more than glacier-derived ones. These rock glaciers were highly correlated with Mean Annual Air Temperature, followed by potential incoming solar radiation and slope. Three machine learning models (Logistic Regression, Random Forest and Support Vector Machines) were trained to generate permafrost probability distribution maps based on their prediction of the probability of rock glaciers being intact as opposed to relict. Logistic Regression and Support Vector Machines were able to produce a similar spatial distribution of permafrost. However, the Random Forest has low precision of spatial variation. The permafrost distribution map suggests the likely occurrence of permafrost to be above 5000 m, indicating a potential for rock and landslides should it thaw in the future. While higher-resolution input data can improve the results, this approach remains promising for application in permafrost regions where information about the ice content of rock glaciers is very limited. |
| format | Article |
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| institution | Kabale University |
| issn | 1674-9847 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | KeAi Communications Co., Ltd. |
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| series | Geodesy and Geodynamics |
| spelling | doaj-art-e133168c4e5c47e7a7c2e6467fdf5ec02025-08-20T03:53:56ZengKeAi Communications Co., Ltd.Geodesy and Geodynamics1674-98472025-05-0116327528710.1016/j.geog.2024.08.003Assessing machine learning models to generate permafrost distribution map in Solukhumbu, NepalArnab Singh0Dibas Shrestha1Kaman Ghimire2Sangya Mishra3Darwin Rana4Sunil Acharya5Central Department of Hydrology and Meteorology, Tribhuvan University, Kathmandu, Nepal; Department of Geography and Environment Studies, Wilfrid Laurier University, Ontario, CanadaCentral Department of Hydrology and Meteorology, Tribhuvan University, Kathmandu, Nepal; Corresponding author.Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaCentral Department of Hydrology and Meteorology, Tribhuvan University, Kathmandu, NepalCentral Department of Hydrology and Meteorology, Tribhuvan University, Kathmandu, NepalCentral Department of Hydrology and Meteorology, Tribhuvan University, Kathmandu, Nepal; Kathmandu Centre for Research and Education, Tribhuvan University-Chinese Academy of Sciences, Kathmandu, NepalPermafrost is one of the key components of the cryosphere. Previous studies show that the extent of permafrost has shifted to higher elevations in Nepal. These researches, however, has been hampered by inconsistency in their study period. Proxies like rock glaciers and climatic variables, such as multi-decadal annual air temperature, are used to link towards the likely occurrence of permafrost. Here, the rock glacier inventory of Solukhumbu was prepared, and classified based on their activity (Intact/Relict) from Google Earth. Talus-based rock glaciers were observed more than glacier-derived ones. These rock glaciers were highly correlated with Mean Annual Air Temperature, followed by potential incoming solar radiation and slope. Three machine learning models (Logistic Regression, Random Forest and Support Vector Machines) were trained to generate permafrost probability distribution maps based on their prediction of the probability of rock glaciers being intact as opposed to relict. Logistic Regression and Support Vector Machines were able to produce a similar spatial distribution of permafrost. However, the Random Forest has low precision of spatial variation. The permafrost distribution map suggests the likely occurrence of permafrost to be above 5000 m, indicating a potential for rock and landslides should it thaw in the future. While higher-resolution input data can improve the results, this approach remains promising for application in permafrost regions where information about the ice content of rock glaciers is very limited.http://www.sciencedirect.com/science/article/pii/S1674984724001010Rock glaciersLogistic regressionRandom forestSupport vector machines |
| spellingShingle | Arnab Singh Dibas Shrestha Kaman Ghimire Sangya Mishra Darwin Rana Sunil Acharya Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal Geodesy and Geodynamics Rock glaciers Logistic regression Random forest Support vector machines |
| title | Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal |
| title_full | Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal |
| title_fullStr | Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal |
| title_full_unstemmed | Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal |
| title_short | Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal |
| title_sort | assessing machine learning models to generate permafrost distribution map in solukhumbu nepal |
| topic | Rock glaciers Logistic regression Random forest Support vector machines |
| url | http://www.sciencedirect.com/science/article/pii/S1674984724001010 |
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