| Issue |
ITM Web Conf.
Volume 19, 2018
Computer Applications in Electrical Engineering (ZKwE’2018)
|
|
|---|---|---|
| Article Number | 01045 | |
| Number of page(s) | 2 | |
| DOI | https://doi.org/10.1051/itmconf/20181901045 | |
| Published online | 21 September 2018 | |
Hybrid forecasting of PM2.5 using SOFM and ELM
IETiSIP, Faculty of Electrical Engeenering, Warsaw University of Technology, Warsaw, Poland
*
Corresponding author: Krzysztof.Siwek@ee.pw.edu.pl
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.