# Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble

arXiv: https://arxiv.org/abs/2312.04330 · HTML: https://mlsanity.com/paper/2312.04330/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Sea Ice Forecasting using Attention-based Ensemble LSTM](https://mlsanity.com/paper/2108.00853/) (2021)
- [MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting](https://mlsanity.com/paper/2308.04511/) (2023)
- [Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation](https://mlsanity.com/paper/1810.03657/) (2018)

## Built on by (most cited first, 1 of 1)

- [SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era](https://mlsanity.com/paper/2502.06753/) (2025)

## Abstract (arXiv)

The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of relatively simple deep learning models with different loss functions for forecasting of spatial distribution for sea ice concentration in the specified water area. Experimental studies confirm the quality of a long-term forecast based on a deep learning model fitted to the specific water area is comparable to resource-intensive physical modeling, and for some periods of the year, it is superior. We achieved a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea.