Application of the principles of systems analysis for the development of environment for integrated air pollution forecasting


  • Lovkin Valerii



air pollution forecasting, nitrogen dioxide, pollutant concentration, machine learning, systems analysis, automobile traffic, long short-term memory, mean absolute error.


Air pollution forecasting is a well-known scientific problem, but nitrogen dioxide is not often considered as a pollutant in the problem. Traffic, presented by vehicle emissions, is one of the main sources of air pollution by nitrogen dioxide. Some existing investigations take traffic into account as input features for forecasting models but traffic is presented by values measured during previous hours. Therefore, separate models are created and trained for every air pollution measurement station. Trained forecasting models are not used systemati-cally and models for forecasting other indicators are not applied. But air pollution over cur-rent hour is caused by traffic mostly at the same place over the same hour but not over previ-ous hours. Therefore, it is important to investigate how usage of forecasted values of traffic impacts on air pollution by nitrogen dioxide in comparison with usage of traffic values meas-ured over previous hours. This task is considered in the paper. The objective of the study is to investigate impact of system approach on accuracy of forecasting air pollution by nitrogen dioxide. Three types of forecasting models based on long short-term memory were created to investigate such an impact. These types include models with concentration of nitrogen dioxide over previous hours as input features and two types of models with additional input features presented by values of traffic over previous hours and by forecasted values of traffic over output hours correspondingly. The last type of models pre-sented application of systems approach, as it uses other models available in the integrated forecasting environment. Experimental investigation allowed to create and train models as well as to compare the results of the models on the datasets representing car traffic and air pollution in Madrid. Application of the approach based on the principles of systems analysis increases accuracy of forecasting air pollution by nitrogen dioxide. It enables to create inte-grated air pollution forecasting environment, which combines information technologies of traffic and air pollution forecasting.


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