The nonlinear autoregressive exogenous neural network performance in predicting Malaysia air pollutant index

Rosminah Mustakim and Mazlina Mamat (2021) The nonlinear autoregressive exogenous neural network performance in predicting Malaysia air pollutant index. Transactions on Science and Technology, 8 (2). pp. 305-310. ISSN 2289-8786

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URL: http://tost.unise.org/pdfs/vol8/no3-2/ToST-CoFA202...

Abstract

Predicting the air quality is important particularly in the areas where air pollution is becoming a major health problem. This paper presents and evaluates the Nonlinear Autoregressive Exogenous (NARX) Neural Network performance in predicting the Air Pollutant Index (API) at three industrial areas in Malaysia: Pasir Gudang, Larkin and TTDI Jaya. The NARX was implemented in an open loop feed-forward architecture and was trained to produce an hour ahead API prediction based on the past values of air quality and meteorological parameters. Six air quality parameters: CO, NO2, O3, PM2.5, PM10, SO2, and three meteorological parameters: wind direction, wind speed and ambient temperature were used as input while the API was set as the output. The prediction performance was measured by using the Coefficient of Determination (R2) and Root Mean Square Error (RMSE) tests. Results show that the performance of NARX model was encouraging with R2 value above 0.97 and RMSE value around 1.21 based on the data collected in 2018 at the three monitoring stations.

Item Type:	Article
Keyword:	Nonlinear Autoregressive Exogenous Neural Network , Malaysia air pollution , Prediction , Time series
Subjects:	T Technology > TD Environmental technology. Sanitary engineering > TD1-1066 Environmental technology. Sanitary engineering > TD878-894 Special types of environment Including soil pollution, air pollution, noise pollution
Department:	FACULTY > Faculty of Engineering
Depositing User:	SAFRUDIN BIN DARUN -
Date Deposited:	22 Jun 2022 11:19
Last Modified:	22 Jun 2022 11:19
URI:	https://eprints.ums.edu.my/id/eprint/32936

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