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.
Highlights
There are many well-known forecasting methods to solve real-world problems; examples of such forecasting methods include the Autoregressive Moving Average Model (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Fuzzy Time Series (FTS), Artificial Neural Networks (ANN), Empirical Mode Decomposition-SVR-Hybrid (EMD-SVR-Hybrid), Empirical Mode Decomposition—Intrinsic Mode Functions-Hybrid (EMD-IMFs-Hybrid), and ANN-Support Vector Machine (SVM)
Markov is a stochastic process with the Markov property that was named after Andrei Andreevich Markov, a Russian mathematician [3]
This study focuses on the haze problem in the Malaysian region of Sarawak
Summary
There are many well-known forecasting methods to solve real-world problems; examples of such forecasting methods include the Autoregressive Moving Average Model (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Fuzzy Time Series (FTS), Artificial Neural Networks (ANN), Empirical Mode Decomposition-SVR-Hybrid (EMD-SVR-Hybrid), Empirical Mode Decomposition—Intrinsic Mode Functions-Hybrid (EMD-IMFs-Hybrid), and ANN-Support Vector Machine (SVM). To clarify the interrelationships of the model, the Markov chain is one of the prominent tools that has been developed to solve complex real-world problems such as prediction of peak energy consumption [1]. The methods have been used to estimate the matrix of transitive from the observing states of the system [4]. It is a random process where all information about the future is contained in the present state. The main components in developing the Markov chain model are state transition matrix and probability; both of which will summarize all the essential parameters of dynamic change. In 2018, there was a serious occurrence of haze due to the forest fires and open burnings in the late evening and at night, and the reading soared to 228 (very unhealthy state) [30]
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