Assessing machine learning and statistical methods for rock glacier-based permafrost distribution in northern Kargil region

Estimating permafrost distribution in high-mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning...

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Bibliographic Details
Published in:Permafrost and Periglacial Processes
Main Authors: Mahanta, K.K., Pradhan, I.P., Gupta, Sharad Kumar, Shukla, D.P.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2024
Subjects:
machine learning
permafrost mapping
rock glacier
SHAP
statistical methods
permafrost
Online Access:https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29259
https://doi.org/10.1002/ppp.2240
Description
Summary:Estimating permafrost distribution in high-mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high-resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA-LST), mean annual normalized difference snow index (MA-NDSI), mean annual normalized difference water index (MA-NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence.

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