Abstract
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.
Highlights
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)
The air pollution caused by dust influences human life and health, and can adversely affect the length and quality of human life [1,2,3]
The measurement data used for experimental research were from the WIOS (Warsaw Inspectorate for Environmental Protection) measurement station located in the residential Ursynow district in Warsaw, Poland measured in the years 2011-2013
Summary
We have observed growing pollution of the natural environment in recent years. As a result of civilization progress and industrial development, the impact of humans activities on the natural environment is growing and, in most cases unfavourable for it. The air pollution caused by dust influences human life and health, and can adversely affect the length and quality of human life [1,2,3]. 1.1 Particulate matters pollutions (combustion), mining, chemical industry, raw and waste landfills, and transport. Air pollution is usually derived from two sources: combustion of poor quality of coal in old, ineffective boilers and domestic furnaces, and from communication sources (engine exhausts) and the street dust carried from under cars. The exceeding the norms can be serious and good forecasting can help to reduce air pollutions
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