Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Particulate matter is a prevalent pollutant that affects human health and the environment. Local authorities need a precise PM10 concentration forecasting model as the information can be used to take precautionary measures and significant actions can be taken to improve air quality status. This study trained and tested the nonlinear model, namely Radial Basis Function (RBF) in an industrial area of Pasir Gudang, Johor. Daily observations of PM10 concentration, meteorological factors (wind speed, ambient temperature, and relative humidity) and gaseous pollutants (SO2, NO2, and CO) from the year 2010-2014 were used in this study. Results showed that RBF model was able to explain 65.2% (R2 = 0.652) and 84.9% (R2 = 0.849) variance in the data during training and testing, respectively. Thus, it is proven that nonlinear model has high ability in virtually representing the complexity and nonlinearity of PM10 in the atmosphere without any prior assumptions.

Original languageEnglish
Title of host publicationInternational Conference on Advances in Civil Engineering and Science Technology, ICACEST 2018
EditorsKok Shien Ng, Siti Hafizan Hassan, Mohd Samsudin Abdul Hamid, Muhamad Hasbullah Hassan Basri, Lyn Dee Goh, Yian Peen Woo
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417380
DOIs
Publication statusPublished - 05 Oct 2018
EventInternational Conference on Advances in Civil Engineering and Science Technology, ICACEST 2018 - Penang, Malaysia
Duration: 05 Sep 201806 Sep 2018

Publication series

NameAIP Conference Proceedings
Volume2020
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

OtherInternational Conference on Advances in Civil Engineering and Science Technology, ICACEST 2018
CountryMalaysia
CityPenang
Period05/09/1806/09/18

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All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this

Abdullah, S., Ismail, M., Ghazali, N. A., & Ahmed, A. N. (2018). Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach. In K. S. Ng, S. H. Hassan, M. S. A. Hamid, M. H. H. Basri, L. D. Goh, & Y. P. Woo (Eds.), International Conference on Advances in Civil Engineering and Science Technology, ICACEST 2018 [020043] (AIP Conference Proceedings; Vol. 2020). American Institute of Physics Inc.. https://doi.org/10.1063/1.5062669