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This is frequently used in computer vision, natural language processing, and other domains to enhance the performance of numerous real-time problems. The purpose of this research is to propose a deep learning-based approach for effectively predicting extreme weather events such as blizzards. To recognize weather patterns and forecast blizzards, the proposed deep learning-based method primarily employs RNN with LSTM. Real-time datasets from the Polar Regions were used to test the proposed approach\u2019s accuracy, and tests were conducted to compare it to existing weather forecasting models. The accuracy of the model is 49.60% (univariate) and 55.19% (bivariate) using bivariate attributes of wind speed and air pressure based on the calculated RMSE values such as 0.0023 and 0.0021.<\/jats:p>","DOI":"10.3233\/jifs-224543","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T11:31:02Z","timestamp":1691148662000},"page":"6797-6812","source":"Crossref","is-referenced-by-count":1,"title":["Blizzard prediction in antarctic meteorological data using deep learning techniques"],"prefix":"10.1177","volume":"45","author":[{"given":"V.S.","family":"Samy","sequence":"first","affiliation":[{"name":"National Centre for Polar and Ocean Research, Goa, India"}]},{"given":"Veena","family":"Thenkanidiyoor","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Goa, 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