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Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

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  • Nurul Nnadiah Zakaria

    (Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia)

  • Mahmod Othman

    (Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia)

  • Rajalingam Sokkalingam

    (Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia)

  • Hanita Daud

    (Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia)

  • Lazim Abdullah

    (School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Malaysia)

  • Evizal Abdul Kadir

    (Faculty of Engineering, Universitas Islam Riau, Pekan Baru 28284, Indonesia)

Abstract

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

Suggested Citation

  • Nurul Nnadiah Zakaria & Mahmod Othman & Rajalingam Sokkalingam & Hanita Daud & Lazim Abdullah & Evizal Abdul Kadir, 2019. "Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak," Sustainability, MDPI, vol. 11(19), pages 1-11, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5190-:d:269576
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    References listed on IDEAS

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    1. Christopher Chatfield, 1973. "Statistical Inference Regarding Markov Chain Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 7-20, March.
    2. Gerelt Tserenjigmid, 2020. "On the characterization of linear habit formation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 70(1), pages 49-93, July.
    3. Chatfield, Chris & Weigend, Andreas S., 1994. "Time series prediction: Forecasting the future and understanding the past : Neil A. Gershenfeld and Andreas S. Weigend, 1994, 'The future of time series', in: A.S. Weigend and N.A. Gershenfeld, eds., ," International Journal of Forecasting, Elsevier, vol. 10(1), pages 161-163, June.
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    Cited by:

    1. Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li, 2022. "Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1483-1511, November.
    2. Shaoqing Geng & Hanping Hou, 2021. "Demand Stratification and Prediction of Evacuees after Earthquakes," Sustainability, MDPI, vol. 13(16), pages 1-22, August.

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