Forecasting Air Pollution Index in Klang by Markov Chain Model
Nurul Nnadiah Zakaria1, Rajalingam Sokkalingam2, Hanita Daud3, Mahmod Othman4
1Nurul Nnadiah Zakaria, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar Perak, Malaysia.
2Rajalingam Sokkalingam, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar Perak, Malaysia.
3Hanita Daud, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar Perak, Malaysia.
5Mahmod Othman, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar Perak, Malaysia.
Manuscript received on 27 September 2019 | Revised Manuscript received on 09 November 2019 | Manuscript Published on 22 November 2019 | PP: 635-639 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11160986S319/19©BEIESP | DOI: 10.35940/ijeat.F1116.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main purpose of analyze future air quality is to maintain the environment in good and healthy condition. Current techniques applied to forecast the air pollution index were ARIMA, SARIMA, Artificial Neural Network, Fuzzy Time Series, Machine Learning, etc. Thus, each technique has its own advantages and disadvantages in the variables, model selection and model accuracy determination. This study aims to forecast air pollution index by developing a Markov Chain model in Klang district, Selangor state which is one of the most polluted area in Malaysia. The Markov Chain model development is a stochastic process sequence that depends on the previous successive event in time. In this model development, state transition matrix and probability are the main concept in determine the future behavior of Air Pollution Index which depends on the present state of the process. The result shows that the developed model is a simple and good performance model that will forecast and evaluate the distribution of the pollution level in long term.
Keywords: Markov Chain, Air Pollution Index (API), Stationary Distribution, Mean Return Time, Long Term Forecasting.
Scope of the Article: Social Sciences