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[2602.12292] A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic
Summary
This paper presents a gradient boosted mixed-model machine learning framework to analyze vessel speed in the U.S. Arctic, utilizing AIS data and environmental factors to improve navigational assessments.
Why It Matters
Understanding vessel speed in the Arctic is critical for navigation and environmental management. This research provides insights into how various factors influence speed, which can inform policy and operational strategies in a changing climate.
Key Takeaways
- A two-stage machine learning approach effectively models vessel speed, distinguishing between zero and positive speed.
- Key determinants of vessel speed include distance to coast and bathymetric depth, with secondary influences from course and vessel group.
- The model achieved strong predictive performance, explaining 77% of variance in speed, which is significant for operational assessments.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.12292 (eess) [Submitted on 31 Jan 2026] Title:A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic Authors:Mauli Pant, Linda Fernandez, Indranil Sahoo View a PDF of the paper titled A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic, by Mauli Pant and 2 other authors View PDF HTML (experimental) Abstract:Understanding how environmental and operational conditions influence vessel speed is crucial for characterizing navigational conditions in the Arctic. We analyzed Automatic Identification System (AIS) data from 2010-2019 to examine vessel speed over ground (SOG). Over half of the AIS records showed zero SOG, and treating zero and positive SOG as a single continuous process can obscure important patterns. We therefore applied a two-stage machine learning framework, first modeling the probability of SOG greater than zero and then modeling SOG conditional on being positive. AIS observations were integrated with sea ice concentration, course over ground, wind, bathymetric depth, distance to coast, vessel group, and navigational status. Gradient boosted decision trees with random effects captured nonlinear environmental responses while accounting for repeated observations. The positive SOG classifier achieved strong discrimination (AUC = 0.85), while the conditional speed model explained approximately 77 percent of out-of-...