(untitled) (Computer systems and information technologies, 2024)
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.