The article discusses the use of a recurrent neural network in the task of predicting pollutants in the air based on simulated data in the form of a time series. Neural recurrent network models with long Short-Term Memory (LSTM) are used to build the forecast. Unidirectional LSTM (hereinafter simply LSTM), as well as bidirectional LSTM (Bidirectional LSTM, hereinafter Bi-LSTM). Both algorithms were applied for temperature, humidity, pollutant concentration, and other parameters, taking into account both seasonal and short-term changes. The Bi-LSTM network showed the best performance and the least errors.

Keywords: environmental monitoring, data analysis, forecasting, recurrent neural networks, long-term short-term memory, unidirectional, bidirectional