IDRIFTNET: Physics-Driven Spatiotemporal Deep Learning for Iceberg Drift Forecasting

TL;DR

IDRIFTNET is a physics-driven deep learning model that accurately forecasts iceberg trajectories by combining analytical physics with neural networks, outperforming existing models even with limited data.

Contribution

The paper introduces IDRIFTNET, a novel hybrid model integrating physics-based formulations with deep learning for improved iceberg drift prediction.

Findings

IDRIFTNET achieves lower FDE and ADE compared to state-of-the-art models.

The model effectively captures complex nonlinear iceberg dynamics.

Performance validated on Antarctic iceberg data.

Abstract

Drifting icebergs in the polar oceans play a key role in the Earth's climate system, impacting freshwater fluxes into the ocean and regional ecosystems while also posing a challenge to polar navigation. However, accurately forecasting iceberg trajectories remains a formidable challenge, primarily due to the scarcity of spatiotemporal data and the complex, nonlinear nature of iceberg motion, which is also impacted by environmental variables. The iceberg motion is influenced by multiple dynamic environmental factors, creating a highly variable system that makes trajectory identification complex. These limitations hinder the ability of deep learning models to effectively capture the underlying dynamics and provide reliable predictive outcomes. To address these challenges, we propose a hybrid IDRIFTNET model, a physics-driven deep learning model that combines an analytical formulation of iceberg drift physics, with an augmented residual learning model. The model learns the pattern of mismatch between the analytical solution and ground-truth observations, which is combined with a rotate-augmented spectral neural network that captures both global and local patterns from the data to forecast future iceberg drift positions. We compare IDRIFTNET model performance with state-of-the-art models on two Antarctic icebergs: A23A and B22A. Our findings demonstrate that IDRIFTNET outperforms other models by achieving a lower Final Displacement Error (FDE) and Average Displacement Error (ADE) across a variety of time points. These results highlight IDRIFTNET's effectiveness in capturing the complex, nonlinear drift of icebergs for forecasting iceberg trajectories under limited data and dynamic environmental conditions.