Open AccessForecasting Air Pollution Index in Klang by Markov Chain Model
Author(s) -
Nurul Nnadiah Zakaria,
Rajalingam Sokkalingam,
Hanita Daud,
Mahmod Othman
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f1116.0986s319
Subject(s) - autoregressive integrated moving average , markov chain , air pollution index , computer science , fuzzy logic , time series , hidden markov model , index (typography) , air quality index , pollution , meteorology , artificial intelligence , machine learning , geography , ecology , world wide web , biology
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.