Iceberg Draft Prediction Using Several Tree-Based Machine Learning Models

The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change. These drifting icebergs present a risk and engineering challenge for subsea installations traversing shallow waters, where iceberg keels may reach the seabed, potentially damaging subsea structures. Consequently, costly and time-intensive iceberg management operations, such as towing and rerouting, are undertaken to safeguard subsea and offshore infrastructure. This study, therefore, explores the application of extra tree regression (ETR) as a robust solution for estimating iceberg draft, particularly in the preliminary phases of decision-making for iceberg management projects. Nine ETR models were developed using parameters influencing iceberg draft. Subsequent analyses identified the most effective models and significant input variables. Uncertainty analysis revealed that the superior ETR model tended to overestimate iceberg drafts; however, it achieved the highest precision, correlation, and simplicity in estimation. Comparison with decision tree regression, random forest regression, and empirical methods confirmed the superior performance of ETR in predicting iceberg drafts.