Work

Sea ice extent data analysis using statistical and unsupervised learning methods

Year: 2025

Type: conference-paper

Abstract: The article analyzes the main trends in the change in Sea Ice extent in the Arctic and Antarctic regions. The impact of Sea Ice extent on climate change and ecosystems in both polar regions and more remote regions of the Earth is analyzed. A statistical analysis of historical data is performed and the Time Series are decomposed into the main components: trends, seasonality, and residuals. To determine the stationarity of the numerical series and test the hypotheses, the Dickey-Fuller test is performed. It is used machine learning methods under unsupervised learning to provide clustering for better understandings of sea ice extent patterns and anomalies in data. The direction of further research is outlined based on the results obtained.

Source: International Workshop on Intelligent Information Technologies & Systems of Information Security (IntelITSIS)

DOI: 10.55056/ceur-ws.org/vol-3963/paper4.pdf

Authors: Vitalii Alekseiko, Vitaly Levashenko, Yurii Voichur, Dmytro Medzatyi

Institutions: Khmelnytskyi National University, University of Žilina

Language: English

Topic: Arctic and Antarctic ice dynamics

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: bronze

OpenAlex ID: W7169754151