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

2019 ◽  
Vol 11 (19) ◽  
pp. 5190 ◽  
Author(s):  
Nurul Nnadiah Zakaria ◽  
Mahmod Othman ◽  
Rajalingam Sokkalingam ◽  
Hanita Daud ◽  
Lazim Abdullah ◽  
...  
Keyword(s):  
Air Pollution ◽  
Markov Chain ◽  
Air Quality ◽  
Markov Chain Model ◽  
Model Development ◽  
Pollution Index ◽  
Pollution Level ◽  
Chain Model ◽  
Air Pollution Index ◽  
Long Run

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.

scholarly journals Forecasting Air Pollution Index in Klang by Markov Chain Model

2019 ◽  
Vol 8 (6S3) ◽  
pp. 535-539
Keyword(s):  
Air Pollution ◽  
Markov Chain ◽  
Markov Chain Model ◽  
Model Development ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Pollution Level ◽  
Chain Model ◽  
Air Pollution Index ◽  
Advantages And Disadvantages

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.

scholarly journals Predicting Daily Air Pollution Index Based on Fuzzy Time Series Markov Chain Model

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 293 ◽  
Author(s):  
Yousif Alyousifi ◽  
Mahmod Othman ◽  
Rajalingam Sokkalingam ◽  
Ibrahima Faye ◽  
Petronio C. L. Silva
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Markov Chain ◽  
Air Quality ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Chain Model ◽  
Air Pollution Index ◽  
Proposed Model

Air pollution is a worldwide problem faced by most countries across the world. Prediction of air pollution is crucial in air quality research since it is related to public health effects. The symmetry concept of fuzzy data transformation from a single point (crisp) to a fuzzy number is essential for the forecasting model. Fuzzy time series (FTS) is applied for predicting air pollution; however, it has a limitation caused by utilizing an arbitrary number of intervals. This study involves predicting the daily air pollution index using the FTS Markov chain (FTSMC) model based on a grid method with an optimal number of partitions, which can greatly develop the model accuracy for air pollution. The air pollution index (API) data, which was collected from Klang, Malaysia, is considered in the analysis. The model has been validated using three statistical criteria, which are the root mean (RMSE), the mean absolute percentage error (MAPE), and the Thiels’ U statistic. Also, the model’s validation has been investigated by comparison with some of the famous statistical models. The results of the proposed model demonstrated outperformed the other models. Thus, the proposed model could be a better option in air pollution forecasting that can be useful for managing air quality.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

Application of Markov Chain Model to Corporate Manpower Planning: A Nigeria Local Government Hub Example

2013 ◽  
Vol 824 ◽  
pp. 514-526 ◽  
Author(s):  
A.C. Igboanugo
Keyword(s):  
Markov Chain ◽  
Local Government ◽  
Markov Chain Model ◽  
Manpower Planning ◽  
Chain Model ◽  
Government Organizations ◽  
Absorbing Markov Chain ◽  
Long Run ◽  
Definite Pattern ◽  
Absorbing States

A corporate manpower planning study, seeking to gain insight into, and hence, attempt tounwrapthe wider meanings of a long-run manpower practice inherent in a set of data obtained from one of the 774 Local Government Organizations in Nigeria, was conducted. The data which spanned over a period of twenty years, relate to six states recruitment, staff stock, training, interdiction, wastage, and retirement and, in particular were found to possess Markov properties, especially stochastic regularity, and therefore had absorbing Markov Chain model fitted into the set. Our results suggest that staff habituate substantial number of times (47) among non-absorbing states before subsequent absorption into any of the two absorbing states. And, again, 52% of the workforce gracefully attain retirement while 48% regrettably get wasted. Agreeably, it seemed that the absorbing Markov Chain model employed has established a definite pattern of manpower flow in the organization as a sure-thing principle rather than a chance mechanism.

scholarly journals Estimating Short- and Long-Term Associations Between Air Quality Index and COVID-19 Transmission: Evidence From 257 Chinese Cities

2021 ◽  
Vol 66 ◽  
Author(s):  
Ru Cao ◽  
Yuxin Wang ◽  
Xiaochuan Pan ◽  
Xiaobin Jin ◽  
Jing Huang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Pollution Level ◽  
Short Term ◽  
Migration Index ◽  
Long Run ◽  
Long Term Impact

Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries.Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations.Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed.Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.

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