CSIT - 2024 рік
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Перегляд CSIT - 2024 рік за Ключові слова "climate zone"
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Документ Information system for earth’s surface temperature forecasting using machine learning technologies(Хмельницький національний університет, 2024) Hovorushchenko, T.; Alekseiko, V.; Shvaiko , V.; Ilchyshyna, J.; Kuzmin, A.Temperature forecasting is a topical issue in many areas of human life. In particular, climate change directly affects agriculture, energy, infrastructure, health care, logistics, and tourism. Anticipating future changes allows you to better prepare for challenges and minimize risks. The paper presents an information system for forecasting the temperature of the Earth’s surface using machine learning technologies. The forecast is formed by a model adapted to the region, by learning on the basis of historical data and tracking the most inherent patterns. The selection and training of the model was carried out on the basis of the analysis of the characteristics of climatic zones, according to the Köppen classification. A comparison of the performance of models for forecasting the average monthly temperatures of the earth’s surface in different climatic zones was carried out. The analysis of scientific publications confirmed the relevance of the chosen research topic. Modern approaches to forecasting climatic indicators are considered. Methods and approaches to temperature forecasting, their advantages and disadvantages are analyzed. The peculiarities of the application of machine learning methods for temperature forecasting are considered, and the criteria for choosing the most accurate and least energy-consuming methods are determined. The research results made it possible to identify machine learning methods that best adapt to temperature patterns and allow accurate short-term forecasting. An approach for long-term forecasting using recurrent neural networks is proposed. An information system has been developed for forecasting future temperatures depending on the climatic features of the studied territories based on the proposed methods. A concept for further research for the development and improvement of the developed information system has been formed.Документ The concept of an information system for forecasting the temperature regime of the earth’s surface based on machine learning(Хмельницький національний університет, 2024) Pavlova, O.; Alekseiko, V.The paper presents the concept of an information system for forecasting the temperature regime of the Earth’s surface using machine learning. Forecasting is based on historical data for a specific area. In order to increase the accuracy of forecasting results, an analysis of the features of climate zones was carried out to identify patterns. A comparison of the dependence of the average earth’s surface monthly temperatures in countries depending on their location in climate zones was carried out. The analysis of sources and scientific publications confirmed the relevance of the chosen research topic. Historical aspects of forecasting changes in climatic indicators are considered. Modern methods and approaches to temperature forecasting, their advantages and disadvantages are analyzed. An overview of the subject area was conducted and the regularities of temperature changes according to climate features were determined. A comparison of temperature regimes for countries located in different climate zones was made. For clarity, graphs of temperature changes were plotted and average indicators were calculated for each climate zone. The results of the study confirm the need to adjust the temperature forecast for certain areas, taking into account their location in a specific climate zone. The revealed regularities in the temperature regime of the countries indicate the need for an individual approach to forecasting and the use of such machine learning methods that are best adapted to the dependencies observed in the climate zone. The architecture of the information system for forecasting future temperatures depending on the climatic features of the studied territories is proposed. A concept has been formed for further research to find more accurate and effective approaches to predicting climate parameters and achieving the goals of sustainable development.