[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121030-en":3,"doc-seo-121030-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121030,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Development of Machine Learning Models for Predicting Average Annual Temperatures","This study evaluates machine learning models for predicting Antarctica’s average annual temperatures, focusing on the accuracy challenge created by remote and highly variable climatic conditions. Four approaches are compared: linear regression, random forest regressor, decision tree regressor, and gradient boosting, using data from multiple Antarctic stations. Findings show that station-specific modeling can outperform general models, while random forest delivers consistently strong results across most metrics. The work supports tailored strategies for environmental modeling and highlights machine learning’s value in climate change forecasting.","Development of machine learning models for predicting average annual temperatures  \nKirill Mukhin1, Viktoriya Erofeeva 1,2, and Zhanna Zhukova2  \n1Peoples’ Friendship university of Russia named after Patrice Lumumba, 115093 Moscow, Russia  \n2Moscow Technical University of Communications and Informatics, 111024 Moscow, Russia  \nAbstract. 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.  \n1 Introduction  \nThe study of climate change, particularly within regions with extreme weather conditions such as Antarctica, stands as an important aspect of modern Earth science. Antarctica, occupying a key position in the global climate system, affects ocean currents [1] and atmospheric processes at the global level. Moreover, the prediction of air temperature in Antarctica possesses an important role in environmental, glaciological and climatological processes. Predicting temperatures in this region is not only a scientific interest, but also a necessity for understanding future climate scenarios on the planet. The development of machine learning technologies introduces new prospects for the accuracy and reliability of forecasts of climatic parameters. This area of machine learning application has gained the importance due to the difficulties in achieving high accuracy of temperature prediction. Specifically, it has been proved that the instability of temperature datasets adheres to intricate, long-range correlation, demonstrating nonlinear behavior [2] .  \nBesides, there are several other problems with predicting temperatures in Antarctica. First, network of weather stations is not stable [3] due to the geographical remoteness of Antarctica, and it is not possible to predict the temperature across the whole territory. Second, the use of machine learning methods is limited by low time resolution, for example, annual or monthly temperature averages [4] .  \nThis study presents the results of the development and comparison of four machine learning models for predicting temperatures in Antarctica: regression models, random forest regressor, decision tree regressor and gradient boosting. Emphasis is placed upon comparing the model performance adapted for specific stations and general models designed for use in  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \na wide range of locations. Encompassing three categories of stations—proximal to the pole, coastal, and intermediary zones between the pole and the coast—the study facilitates to assess the impact of geographical location on the accuracy of predictions.  \nThe primary objective of this study is not only to demonstrate the prospects of machine learning in predicting temperatures in the extreme conditions of Antarctica, but also to identify the most effective modeling approaches, considering the diversity of climatic conditions in different parts of the continent. By analyzing specialized and general models, this work aims to contribute to the optimization of machine learning strategies for climate research.  \n2 Methodology  \nIn order to develop machine learning models for predicting chang","cbCaibxyFB44Game","https://ap.wps.com/l/cbCaibxyFB44Game","pdf",202874,1,5,"English","en",105,"# Introduction\n# Methodology\n## Feature engineering\n## Application of machine learning algorithms","[{\"question\":\"Which machine learning models were compared for Antarctica temperature prediction?\",\"answer\":\"The study compares linear regression, random forest regressor, decision tree regressor, and gradient boosting.\"},{\"question\":\"Why is predicting temperatures in Antarctica considered difficult?\",\"answer\":\"The research highlights the instability of station networks due to geographic remoteness and the limited temporal resolution of available data such as annual or monthly averages.\"},{\"question\":\"What is the main conclusion about model performance?\",\"answer\":\"Results indicate that models tailored to specific stations can improve prediction accuracy, and the random forest model shows exceptional performance across most evaluation metrics.\"}]","Development of Machine Learning Models for Predicting Average Annual Temperatures | 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machine learning models were compared for Antarctica temperature prediction?","Question",{"text":75,"@type":76},"The study compares linear regression, random forest regressor, decision tree regressor, and gradient boosting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is predicting temperatures in Antarctica considered difficult?",{"text":80,"@type":76},"The research highlights the instability of station networks due to geographic remoteness and the limited temporal resolution of available data such as annual or monthly averages.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main conclusion about model performance?",{"text":84,"@type":76},"Results indicate that models tailored to specific stations can improve prediction accuracy, and the random forest model shows exceptional performance across most evaluation 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