Prediction of gas concentrations based on a recurrent neural network
Abstract
Prediction of gas concentrations based on a recurrent neural network
Incoming article date: 17.05.2024The 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