The fuzzy logic in air pollution forecasting ‎model

In the paper a model to predict the concentrations of particulate matter PM10, PM2.5, SO2, NO, CO and O3 for a chosen number of hours forward is proposed. The method requires historical data for a large number of points in time, particularly weather forecast data, actual weather data and pollution data. The idea is that by matching forecast data with similar forecast data in the historical data set it is possible then to obtain actual weather data and through this pollution data. To aggregate time points with similar forecast data determined by a distance function, fuzzy numbers are generated from the forecast data, covering forecast data and actual data. Again using a distance function, actual data is compared with the fuzzy number to determine how the grade of membership. The model was prepared in such a way that all the data which is usually imprecise, chaotic, uncertain can be ‎used.‎