Robust empirical Bayes approach for Markov chain modeling of air pollution index

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.

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Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model

Environmental Monitoring and Assessment ◽

10.1007/s10661-020-08666-8 ◽

2020 ◽

Vol 192 (11) ◽

Author(s):

Yousif Alyousifi ◽

Kamarulzaman Ibrahim ◽

Wei Kang ◽

Wan Zawiah Wan Zin

Keyword(s):

Air Pollution ◽

Markov Chain ◽

Temporal Dynamics ◽

Markov Chain Model ◽

Pollution Index ◽

Chain Model ◽

Air Pollution Index ◽

Spatial Markov Chain ◽

Spatio Temporal

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Correction to: Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model

Environmental Monitoring and Assessment ◽

10.1007/s10661-020-08720-5 ◽

2020 ◽

Vol 192 (12) ◽

Author(s):

Yousif Alyousifi ◽

Kamarulzaman Ibrahim ◽

Wei Kang ◽

Wan Zawiah Wan Zin

Keyword(s):

Air Pollution ◽

Markov Chain ◽

Temporal Dynamics ◽

Markov Chain Model ◽

Pollution Index ◽

Chain Model ◽

Air Pollution Index ◽

Spatial Markov Chain ◽

Spatio Temporal

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

Symmetry ◽

10.3390/sym12020293 ◽

2020 ◽

Vol 12 (2) ◽

pp. 293 ◽

Cited By ~ 4

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.

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Forecasting Air Pollution Index in Klang by Markov Chain Model

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1116.0986s319 ◽

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.

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An Empirical Bayes Approach to Topic Modeling

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412837 ◽

2021 ◽

Author(s):

Anirban Gangopadhyay

Keyword(s):

Topic Modeling ◽

Empirical Bayes ◽

Empirical Bayes Approach ◽

Bayes Approach

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An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2020-0026 ◽

2021 ◽

Vol 20 (1) ◽

pp. 1-15

Author(s):

Qi Zhang ◽

Zheng Xu ◽

Yutong Lai

Keyword(s):

Long Range ◽

Latent Variables ◽

Empirical Bayes ◽

Genome Structure ◽

Peak Detection ◽

Data Matrix ◽

Dispersion Modeling ◽

Empirical Bayes Approach ◽

Over Dispersion ◽

Bayes Approach

Abstract Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the “true” interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

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