Forecasting meteorological indicators using neural network technologies

The article discusses the use of neural network technologies for forecasting meteorological indicators in Moscow. The relevance of the study is grounded on the need to improve the accuracy and reliability of temperature forecasts, which is becoming especially important in the context of climate change and the growth of extreme weather events. The methodology includes collecting and processing meteorological data, setting up and training a neural network model using the Python programming language and related libraries. As a final result, the article provides a working model for forecasting meteorological indicators, tested on the average absolute error and the determination coefficient.