Development of machine learning models for predicting average annual temperatures
This study assesses machine learning models for predicting Antarctica's average annual temperatures, addressing the challenge of accuracy in remote and variable climatic conditions. Four models were compared: linear regression, random forest regressor, decision tree regressor, and gradient boos...
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ftdoajarticles:oai:doaj.org/article:0e8f89b2d09345e39682867ea0907703 2024-09-15T17:48:09+00:00 Development of machine learning models for predicting average annual temperatures Mukhin Kirill Erofeeva Viktoriya Zhukova Zhanna 2024-01-01T00:00:00Z https://doi.org/10.1051/e3sconf/202454204002 https://doaj.org/article/0e8f89b2d09345e39682867ea0907703 EN FR eng fre EDP Sciences https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/72/e3sconf_yifhg2024_04002.pdf https://doaj.org/toc/2267-1242 2267-1242 doi:10.1051/e3sconf/202454204002 https://doaj.org/article/0e8f89b2d09345e39682867ea0907703 E3S Web of Conferences, Vol 542, p 04002 (2024) Environmental sciences GE1-350 article 2024 ftdoajarticles https://doi.org/10.1051/e3sconf/202454204002 2024-08-05T17:48:51Z This study assesses machine learning models for predicting Antarctica's average annual temperatures, addressing the challenge of accuracy in remote and variable climatic conditions. Four models were compared: linear regression, random forest regressor, decision tree regressor, and gradient boosting, utilizing data from diverse Antarctic stations. Results indicate the superiority of specific models tailored to individual stations, with the random forest model demonstrating exceptional performance across most metrics. This emphasizes the significance of geographical specificity in improving climate prediction accuracy. The research underscores machine learning's potential in climate change forecasting, advocating for tailored approaches in environmental modeling. Article in Journal/Newspaper Antarc* Antarctic Directory of Open Access Journals: DOAJ Articles E3S Web of Conferences 542 04002 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English French |
topic |
Environmental sciences GE1-350 |
spellingShingle |
Environmental sciences GE1-350 Mukhin Kirill Erofeeva Viktoriya Zhukova Zhanna Development of machine learning models for predicting average annual temperatures |
topic_facet |
Environmental sciences GE1-350 |
description |
This study assesses machine learning models for predicting Antarctica's average annual temperatures, addressing the challenge of accuracy in remote and variable climatic conditions. Four models were compared: linear regression, random forest regressor, decision tree regressor, and gradient boosting, utilizing data from diverse Antarctic stations. Results indicate the superiority of specific models tailored to individual stations, with the random forest model demonstrating exceptional performance across most metrics. This emphasizes the significance of geographical specificity in improving climate prediction accuracy. The research underscores machine learning's potential in climate change forecasting, advocating for tailored approaches in environmental modeling. |
format |
Article in Journal/Newspaper |
author |
Mukhin Kirill Erofeeva Viktoriya Zhukova Zhanna |
author_facet |
Mukhin Kirill Erofeeva Viktoriya Zhukova Zhanna |
author_sort |
Mukhin Kirill |
title |
Development of machine learning models for predicting average annual temperatures |
title_short |
Development of machine learning models for predicting average annual temperatures |
title_full |
Development of machine learning models for predicting average annual temperatures |
title_fullStr |
Development of machine learning models for predicting average annual temperatures |
title_full_unstemmed |
Development of machine learning models for predicting average annual temperatures |
title_sort |
development of machine learning models for predicting average annual temperatures |
publisher |
EDP Sciences |
publishDate |
2024 |
url |
https://doi.org/10.1051/e3sconf/202454204002 https://doaj.org/article/0e8f89b2d09345e39682867ea0907703 |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
E3S Web of Conferences, Vol 542, p 04002 (2024) |
op_relation |
https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/72/e3sconf_yifhg2024_04002.pdf https://doaj.org/toc/2267-1242 2267-1242 doi:10.1051/e3sconf/202454204002 https://doaj.org/article/0e8f89b2d09345e39682867ea0907703 |
op_doi |
https://doi.org/10.1051/e3sconf/202454204002 |
container_title |
E3S Web of Conferences |
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542 |
container_start_page |
04002 |
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1810289297947885568 |