Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)

Authors

  • Nor Hayati Binti Shafii Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Rohana Alias Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nur Fithrinnissaa Zamani Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nur Fatihah Fauzi Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v5i3.149

Keywords:

API, Support Vector Machine (SVM), time series forecasting, kernel function, PM2.5

Abstract

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development.  Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health.  The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM).  The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.

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References

Arampongsanuwat, S., & Meesad, P. (2011). Prediction of PM10 using Support Vector Regression. International Conference on Information and Electronics Engineering, IACSIT Press. Singapore.

Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental pollution, 151(2), 362-367.

Lu, W.-Z., & Wang, W.-J. (2005). Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere, 59(5), 693-701.

MAMPU, M. s. O. D. P. (2019). Bacaan Indeks Pencemar Udara (IPU) bagi negeri Kelantan tahun 2018. Retrieved Nov 01, 2019 from http://www.data.gov.my/data/en_US/dataset/bacaan-indeks-pencemar-udara-ipu-negeri-kelantan-bagi-tahun-2017/resource/25bbf752-661b-4445-8959-2ef45eaf1dfe

Oloruntoba, S., & Akinode, J. (2017). Student academic performance prediction using support vector machine. International Journal of Engineering Sciences and Research Technology, 6(12), 588-597.

Vaiz, J. S., & Ramaswami, M. (2016). A Hybrid Model to Forecast Stock Trend Using Support Vector Machine and Neural Networks.

Vinagre, E., Pinto, T., Ramos, S., Vale, Z., & Corchado, J. M. (2016). Electrical energy consumption forecast using support vector machines. 27th International Workshop on Database and Expert Systems Applications (DEXA), 2016.

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Published

2020-10-18

How to Cite

Shafii, N. H. B., Alias, R. ., Zamani, N. F., & Fauzi, N. F. (2020). Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM). Journal of Computing Research and Innovation, 5(3), 43–53. https://doi.org/10.24191/jcrinn.v5i3.149

Issue

Section

General Computing