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Title

Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model

Year: 1401
COI: JR_GJESM-8-1_003
Language: EnglishView: 84
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Authors

M.I. Rumaling - Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
F.P. Chee - Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
H.W.J. Chang - Preparatory Centre for Science and Technology, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
C.M. Payus - Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
S.K. Kong - Department of Atmospheric Sciences, National Central University, Taoyuan, ۳۲۰۰۱, Taiwan
J. Dayou - Energy, Vibration and Sound Research Group, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
J. Sentian - Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia

Abstract:

BACKGROUND AND OBJECTIVES: Air quality in some developing countries is dominated by particulate matter, especially those with size ۱۰ micrometers and smaller or PM۱۰. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM۱۰ concentration forecasting is important for early prevention and in urban development planning, which is crucial for developing cities. This paper presents the development of PM۱۰ forecasting model using nonlinear autoregressive with exogenous input model.METHODS: To improve performance of nonlinear autoregressive with exogenous input model, principal component analysis is used prior to the model for variable selection. The first stage of principal component analysis involves Scree plot, which determines the number of principal components based on explained variance. This is then followed by selecting variables using a rotated component matrix, based on their strength of contribution towards variation of PM۱۰ concentration. To test the model, PM۱۰ data in Kota Kinabalu from ۲۰۰۳ – ۲۰۱۰ was used. Neural network models are developed using this data by varying number of input variables with the inclusion of temporal variables. The developed forecasting models are evaluated using data PM۱۰ in the city from ۲۰۱۱ to ۲۰۱۲. Four performance indicators, namely root mean square error, mean absolute error, index of agreement and fractional bias are reported.FINDINGS: 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 PM۱۰ 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 ۷.۰۸۶±۰.۸۷۳ µg/m۳. The selection of significant variables helps in reducing input variables inside the forecast model without degrading its forecast performance.CONCLUSION: This model shows very promising performance in forecasting PM۱۰ concentration in Kota Kinabalu as it requires fewer input variables and does not require variable transformation.

Keywords:

artificial neural network (ANN) , Nonlinear autoregression with exogenous input (NARX) , Principal Component Analysis (PCA) , Rotated component matrix , Scree plot

Paper COI Code

This Paper COI Code is JR_GJESM-8-1_003. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/1259896/

How to Cite to This Paper:

If you want to refer to this Paper in your research work, you can simply use the following phrase in the resources section:
Rumaling, M.I. and Chee, F.P. and Chang, H.W.J. and Payus, C.M. and Kong, S.K. and Dayou, J. and Sentian, J.,1401,Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model,https://civilica.com/doc/1259896

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