Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

On the Artificial Intelligence for the Ocean Modeling and Forecast along the Northern Sea Route
Aleksei Buinyi1, Ekaterina Zhdanova1, Aleksandr Evdokimov1,2, Natalia Sukhikh1
1 Marine Information Technologies

Moscow, Russia

2 Marine Research Center, Lomonosov Moscow State University

Moscow, Russia

work on results of numerical modeling (WaveWatchIII). However, Aliet al. (2021) concluded that ARIMA performed the worst results in theirprediction of significant wave height. The best results were shown byGRU (Gated Recurrent Units) of the deep learning model. They also
attempted to use LSTM (Long Short Time Memory) model. There are anumber of publications that proposed a hybrid approach for forecastingin geosciences and networks training. Tsai et al. (2021) described a
differential parameter learning (dPL) framework to train models withcomplex nonlinear and non-stationary data. Tang et al. (2021) publisheda multi-component hybrid model for wave height prediction. Wu et al.
(2020) proposed a hybrid physics-based machine learning model andsuccessfully trained it with wind waves data. Also, CNN (ConvolutionalNeural Network) and RNN (Recurrent Neural Network) might be usedin geosciences. Wang et al. (2020) applied CNN to get the forecast of thecyclone track.

ABSTRACT
Nowadays, the problem of short- and long-term forecasts in the Russian
Arctic becomes important for global logistics and engineering. Here, we
present our first findings on the application of machine learning (ML)methods to oceanographic data. The Barents Sea is the region of ourinterest, and we focus on the analysis of wind waves and surface currents.
We applied several ML models to reproduce observed time series andproduced short- and long-term forecasts. LSTM model and XGBoost
algorithms showed better results in fitting the observed curve. At thecurrent stage of our research, SARIMA showed better performance inforecasting.

KEY WORDS: Machine learning, wind waves; currents; prediction;SARIMA; LSTM.

There are a lot of promising results in geosciences now. We chose themost popular ML approaches to test their ability in Arctic forecasts. In
the current work, we test all algorithms on wind waves and surface
currents data. The results showed the SARIMA model performed betteron both, shorter and longer, timescales. However, forecast uncertainties
increase significantly with time. The RNN produces promising results in
detecting spatial patterns, but its application in forecasting will be doneat the next stage of our research. XGBoost algorithms performed betterin curve fitting.

INTRODUCTION
The Russian Arctic changes rapidly due to modern climate change. It
significantly influences global economics and logistics. The Northern
Sea Route is the key transport way here. But the region is stillcharacterized by harsh hydrometeorological and ice conditions during
the autumn-winter season. It is still poorly covered by regularobservations. Nowadays, the problem of short- and long-term forecasting
becomes important for logistics, operations, and engineering. Numericalmodeling and forecasting of atmospheric, oceanic, and ice conditions
should help with planning safe and cost-effective routes.

DATASETS
Mainly, two datasets are used. We took significant wave height data fromglobal reanalysis ERA-5 with 0.25°×0.25° spatial and 1 hour timeresolution (Copernicus Climate Change Service (C3S), 2017). Surfacecurrents are taken from 1/12° global ocean reanalysis GLORYS with 1day time resolution (Lellouche et al., 2021). We chose the Barents Sea
as the test region and two points there for detailed analysis (Figure 1).
The first point belongs to the open ocean, and the second is placed in theshallow waters of Chosha Bay. These two regions are important forindustry and polar engineering. There are several months of wind waves
and ocean currents observations at these points and at the next stages, weare going to compare our results with in-situ observations. The ERA5

In response to the request, at the Marine Research Center, we develop acomplex forecasting system using numerical modeling and artificialintelligence (AI) methods. Recently, the application of machine learning
methods in oceanography and geosciences becomes more and morepopular. Adyta et al. (2018) used the ARIMA model (Autoregressive
Integrated Moving Average) to predict significant waves height obtainedfrom SWAN numerical model with a high correlation exceeding 0.94.
The results showed an accurate short-term prediction with ARIMA(2,2,2). Yang et al. (2019) described SARIMA (Seasonal Autoregressive
Integrated Moving Average) as a useful tool for long-term prediction ofsignificant wave height in the South China Sea. They also based their2061