Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model

Rumaling, M.I., Chee, F.P., Chang, H.W.J., Payus, C.M., Kong, S.K., Dayou, J. and Sentian, J., (2021). Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model. Global Journal of Environmental Science and Management, 8(1), 27-44

Document type:
Article
Collection:
UNU-IAS Journal Articles

Metadata
Documents
Links
Versions
Statistics
  • Attached Files (Some files may be inaccessible until you login with your UNU Collections credentials)
    Name Description MIMEType Size Downloads
    GJESM_Volume_8_Issue_1_Pages_27-44.pdf GJESM_Volume 8_Issue 1_Pages 27-44.pdf application/pdf 987.45KB
  • Sub-type Journal article
    Author Rumaling, M.I.
    Chee, F.P.
    Chang, H.W.J.
    Payus, C.M.
    Kong, S.K.
    Dayou, J.
    Sentian, J.
    Title Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model
    Appearing in Global Journal of Environmental Science and Management
    Volume 8
    Issue No. 1
    Publication Date 2021-09
    Place of Publication Online
    Publisher GJESM Publisher, Global Journal of Environmental Science and Management
    Start page 27
    End page 44
    Language eng
    Abstract Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM10 concentration forecasting is important for early prevention and in urban development planning, which is crucial for developing cities. This paper presents the development of PM10 forecasting model using nonlinear autoregressive with exogenous input model. Results from principal component analysis show that five variables including wind direction index, relative humidity, ambient temperature, concentration of nitrogen dioxide, and concentration of ozone strongly contribute to the variation of PM10 concentration. By using these variables together with temporal variables as input in the nonlinear autoregressive with exogenous input models, the resultant model shows good forecasting performance, with root mean square error of 7.086±0.873 µg/m3.
    Keyword Artificial neural network (ANN)
    Nonlinear autoregression with exogenous input (NARX)
    Principal component analysis (PCA)
    Rotated component matrix
    Scree plot
    Copyright Holder Global Journal of Environmental Science and Management (GJESM)
    Copyright Year 2022
    Copyright type All rights reserved
    DOI 10.22034/GJESM.2022.01.03
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 264 Abstract Views, 170 File Downloads  -  Detailed Statistics
    Created: Tue, 21 Sep 2021, 12:43:02 JST by Miwa Higashimuki on behalf of UNU IAS