Hybrid forecasting of PM2.5 using SOFM and ELM

The article presents a new approach to atmospheric PM2.5 dust prediction using an Extreme Learning Machine (ELM) neural network with clusterization done by the Self Organizing Feature Map (SOFM). This work is concerned with the calculation of the average level of air particulate matters PM2,5 in Warsaw's Ursynow one day ahead. The brief description of the hazards posed by air pollution is included. The work presents a short description of the SOFM and ELM networks, and their hybridized system used as a prediction tool. The analysis of the obtained results was presented and discussed.