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

Equipping roads with communications is complicated by the almost complete lack of roadside infrastructure, including power lines, as well as difficult terrain. When emergencies occur on this kind of country roads, residents are forced to seek help from nearby settlements that are well-connected. Therefore, providing suburban routes with communications is a key social task. Using an existing base station as an example, this article calculates the attenuation and propagation range of a radio signal for LTE technology and GSM technology, provides a comparative analysis, and uses methods of mathematical modeling and system analysis.

Keywords: LTE, GSM, Okumura-Hata model, Lee model, Longley-Rice model

The article discusses the use of a recurrent neural network in the problem of forecasting pollutants in the air based on actual data in the form of a time series. A description of the network architecture, the training method used, and the method for generating training and testing data is provided. During training, a data set consisting of 126 measurements of various components was used. As a result, the quality of the conclusions of the resulting model was assessed and the averaged coefficients of the MSE metric were calculated.

Keywords: air pollution, forecasting, neural networks, machine learning, recurrent network, time series analysis