title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÐºÑÐ°ÑÐºÐ¾ÑÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð° Ð¿Ð¾Ð³Ð¾Ð´Ñ Ð½Ð° Ð±Ð°Ð·Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a short-term weather forecast algorithm based on a neural network
Creators: name: ÐÑÐ·Ð½ÐµÑÐ¾Ð²Ð°, Ð®Ð»Ð¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
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ÑÐ¿ÑÐ°Ð²Ð»ÑÐµÐ¼ÑÐ¹ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÐ¹ Ð½ÐµÐ¹ÑÐ¾Ð½
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐµÐ¾ÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð²
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artificial neural network
recurrent neural network
neural network with long short-term memory
gated recurrent unit
prediction of meteorological parameters
interpolation

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description: The relevance of this work lies in the need to develop forecasting models based on neural networks for portable meteorological stations, due to the inability to use resource-intensive mathematical models for local research. The object of the study is methods based on neural networks for forecasting meteorological parameters. The purpose of this work is to develop an effective model of a neural network with a long short-term memory for forecasting meteorological elements of the weather. In order to achieve the goal set in the work, theoretical material on recurrent neural networks was studied, two approaches to creating a forecast were developed: using interpolation of the predicted data obtained and creating a training set using interpolation.In accordance with the task, a neural network with a long short-term memory was implemented using the TensorFlow framework and the Pandas, Keras, SKlearn bibliotech in the Python programming language, and a comparative analysis of meteorological data forecasting methods was carried out. The forecast using this model reaches an accuracy of up to 90% for a number of meteorological parameters. The neural network has shown that it is able to search for periodicity in data and at the same time is able to predict the seasonal trend.