Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach
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 stud...
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American Institute of Physics Inc.
2023
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| _version_ | 1833350615812538368 |
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| author | Abdullah S. Ismail M. Ghazali N.A. Ahmed A.N. |
| author2 | 56509029800 |
| author_facet | 56509029800 Abdullah S. Ismail M. Ghazali N.A. Ahmed A.N. |
| author_sort | Abdullah S. |
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| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. � 2018 Author(s). |
| format | Conference Paper |
| id | my.uniten.dspace-23657 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | American Institute of Physics Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-236572023-05-29T14:50:48Z Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach Abdullah S. Ismail M. Ghazali N.A. Ahmed A.N. 56509029800 57210403363 26430938300 57214837520 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. � 2018 Author(s). Final 2023-05-29T06:50:48Z 2023-05-29T06:50:48Z 2018 Conference Paper 10.1063/1.5062669 2-s2.0-85055571664 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055571664&doi=10.1063%2f1.5062669&partnerID=40&md5=4e204f5ec338c952a81fe27a38dad1a9 https://irepository.uniten.edu.my/handle/123456789/23657 2020 20043 All Open Access, Bronze American Institute of Physics Inc. Scopus |
| spellingShingle | Abdullah S. Ismail M. Ghazali N.A. Ahmed A.N. Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach |
| title | Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach |
| title_full | Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach |
| title_fullStr | Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach |
| title_full_unstemmed | Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach |
| title_short | Forecasting particulate matter (PM10) concentration: A radial basis function neural network approach |
| title_sort | forecasting particulate matter (pm10) concentration: a radial basis function neural network approach |
| url_provider | http://dspace.uniten.edu.my/ |