VAE Based Non-Autoregressive Transformer Model for Sea Ice Concentration Forecast
Paper in proceeding, 2022

Arctic shipping along the Northern Sea Route (NSR) relies on reliable and precise sea ice concentration (SIC) information. However, the SIC forecast used to assist NSR shipping is often inaccurate. This study proposes a VAE based Non-Autoregressive Transformer model for the long-term SIC forecast task. The proposed model ensembles the deep generative model of Variational Autoencoder and the deep learning model of Transformer to overcome the temporal delay and accumulative error problems shown in traditional time series models. Model validation has been conducted to compare the forecasts of SIC with other machine learning and deep learning models. The proposed model outperforms the compared models in terms of different metrics. The proposed VAE based Non-Autoregressive Transformer can be used for long-term SIC forecast and achieve stable and good accuracy predictions.

Variational Autoencoder

Arctic shipping

Transformer

Non autoregressive model.

Sea ice concentration

Time series analysis

Author

Da Wu

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Xiao Lang

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Wengang Mao

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Di Zhang

Wuhan University of Technology

Jinfen Zhang

Wuhan University of Technology

Rong Liu

Wuhan University of Technology

Proceedings of the International Offshore and Polar Engineering Conference

10986189 (ISSN) 15551792 (eISSN)


978-1-880653-81-4 (ISBN)

The 32nd International Ocean and Polar Engineering Conference
Shanghai, China,

Explore innovative solutions for arctic shipping

The Swedish Foundation for International Cooperation in Research and Higher Education (STINT) (Dnr:CH2016-6673), 2017-05-01 -- 2020-06-30.

Driving Forces

Sustainable development

Areas of Advance

Transport

Subject Categories

Marine Engineering

Probability Theory and Statistics

Other Electrical Engineering, Electronic Engineering, Information Engineering

ISBN

9781880653814

More information

Latest update

11/25/2022