Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble

Autor:	Borisova, Julia, Nikitin, Nikolay O.
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Physics - Atmospheric and Oceanic Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.04330 Zobrazit plný text záznamu View this record from Arxiv