A Machine Learning Framework to Automate the Classification of Surge‐Type Glaciers in Svalbard

Abstract Surge‐type glaciers are present in many cold environments in the world. These glaciers experience a dramatic increase in velocity over short time periods, the surge, followed by an extended period of slow movement, the quiescence. This study aims at understanding why only few glaciers exhibit a transient behavior. We develop a machine learning framework to classify surge‐type glaciers, based on their location, exposure, geometry, climatic mass balance and runoff. We apply this approach to the Svalbard archipelago, a region with a relatively homogeneous climate. We compare the performance of logistic regression, random forest, and extreme gradient boosting (XGBoost) machine learning models that we apply to a newly combined database of glaciers in Svalbard. Based on the most accurate model, XGBoost, we compute surge probabilities along glacier centerlines and quantify the relative importance of several controlling features. Results show that the surface and bed slopes, ice thickness, glacier width, climatic mass balance, and runoff along glacier centerlines are the most significant features explaining surge probability for glaciers in Svalbard. A thicker and wider glacier with a low surface slope has a higher probability to be classified as surge‐type, which is in good agreement with the existing theories of surging. Finally, we build a probability map of surge‐type glaciers in Svalbard. The framework shows robustness on classifying surge‐type glaciers that were not previously classified as such in existing inventories but have been observed surging. Our methodology could be extended to classify surge‐type glaciers in other areas of the world.