Predicting PM_{2.5} Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City

- Journal title : Asian Journal of Atmospheric Environment
- Volume 10, Issue 2, 2016, pp.67-79
- Publisher : Korean Society for Atmospheric Environment
- DOI : 10.5572/ajae.2016.10.2.067

Title & Authors

Predicting PM_{2.5} Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City

Asadollahfardi, Gholamreza; Zangooei, Hossein; Aria, Shiva Homayoun;

Asadollahfardi, Gholamreza; Zangooei, Hossein; Aria, Shiva Homayoun;

Abstract

The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the greatest concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, , , CO, and were used as inputs to the artificial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting concentrations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coefficient of determination (), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of pollution is predictable using a Markov chain method.

Keywords

Air pollution; concentration prediction;Artificial neural network;Markov chain;

Language

English

Cited by

References

1.

Bahari, R.A., Ali Abssaspour, R., Pahlavi, P. (2014) Prediction of $PM_{2.5}$ concentrations using temperature inversion effects based on an artificial neural network, The ISPRS international conference of Geospatial information research, 15-17 November, Tehran, Iran.

2.

Caputo, M., Gimenez, M., Schlamp, M. (2003) Intercomparison of atmospheric dispersion models. Atmospheric Environment 37, 2435-2449.

3.

Chung, K.L., Farid AitSahlia (2003) Elementary Probability Theory: With Stochastic Processes and an Introduction to Mathematical Finance, Springer Undergraduate Texts in Mathematics and Technology, ISSN 0172-6056.

4.

Cohen, S., Intrator, N. (2002) Automatic model selection in a hybrid perceptron/radial network; Information Fusion. Special Issue on Multiple Experts 3(4), 259-266.

5.

Deng, X., Zhang, F., Rui, W., long, F., Wang, L., Feng, Z., Chen, D., Ding, W. (2013) $PM_{2.5}$ -induced oxidative stress triggers autophagy in human lung epithelial A549 cells. Toxicology in Vitro 27(6), 1762-1770.

6.

Dong, G.H., Zhang, P., Sun, B., Zhang, L., Chen, X., Ma, N. (2012) Long term exposure to ambient air pollution and respiratory disease mortality in Shenyang, China: a 12 year population - based retrospective cohort study. Respiration 84(5), 360-368.

7.

Eleuteri, A., Tagliaferri, R., Milano, L. (2005) A novel information geometric approach to variable selection in MLP networks. Neural Network 18(10), 1309-1318.

8.

Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J. (2015) Artificial neural network forecasting of $PM_{2.5}$ pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment 107, 118-128.

9.

Goss, C.H., Newsom, S.A., Schildcrout, J.S., Sheppard, L., Kaufman, J.D. (2004) Effect of ambient air pollution on pulmonary exacerbations and lung function in cystic fibrosis. American Journal of Respiratory and Critical Care Medicine 169(7), 816-821.

10.

Hambli, R. (2011) Multiscale prediction of crack density and crack length accumulation in trabecular bone based on neural networks and finite element simulation. International Journal for Numerical Methods in Biomedical Engineering 27(4), 461-475.

11.

Hanna, S.R., Paine, R., Heinold, D., Kintigh, E., Baker, D. (2007) Uncertainties in air toxics calculated by the dispersion models AERMOD and ISCST 3 in the Houston ship channel area. Journal of Applied Meteorology and Climatology 46, 1372-1382.

12.

Harsham, D.K., Bennett, M. (2008) A sensitivity study of validation of three regulatory dispersion models. American Journal of Environmental Sciences 4(1), 63-76.

13.

Haykin, S. (1999) Neural networks: a comprehensive foundation. (2nd ed.) Upper Saddle River, New Jersey: Prentice Hal.

14.

Jones, R.M., Nicas, M. (2014) Benchmarking of a Markov multizone model of contaminant transport. Annals of Occupational Hygiene 58(8), 1018-1031.

15.

Kohavi, R., John, G.H. (1997) Wrappers for feature subset selection. Artificial Intelligence 97, 273-324.

16.

Kohohen, T. (1984) Self-organization and associative memory. New York: Springer-Verlag.

17.

Krause, P., Boyle, D.P., Base, F. (2005) Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences 5, 89-97.

18.

Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Li, P., Xin, J.Y., Wang, Y.S., Wang, S.G., Li, G.X., Pan, X.C., Liu, Z.R., Wang, L.L. (2015) Reinstate regional transport of $PM_{2.5}$ as a major cause of severe haze in Beijing. Proceeding of the National Academy of Sciences of the United States of America 112, E2739-E2740.

19.

Kuncheva, L. (2004) Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York, USA.

20.

Kurt, A., Gulbagci, B., Karaca, F., Alagha, O. (2008) An online air pollution forecasting system using neural networks. Environment International 34, 592-598.

21.

Logofet, D.O., Lensnaya, E.V. (2000) The mathematics of Markov models: what Markov chains can really predict in forest successions. Ecological Modelling 2(3), 285-298.

22.

Nicas, M. (2014) Markov modeling of contaminant concentrations in indoor air. American Journal of Environmental Sciences, 61(4), 484-491.

23.

Niska, H., Dorling, S., Chatterton, T., Foxall, R., Cawley, G. (2003) Extensive evaluation of neural network models for the prediction of $NO_2$ and $PM_{10}$ concentrations, compared with a deterministic modeling system and measurements in central Helsinki. Atmospheric Environment 37, 4539-4550.

24.

Niska, H., Heikkinen, M., Kolehmainen, M. (2006) Genetic algorithms and sensitivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series. Chapter Intelligent data engineering and automated learning-IDEAL2006 volume 4224 of the series lecture notes in computer science pp. 224-231 springer publisher.

25.

Niska, H., Rantamaki, M., Hiltunen, T., Karppinen, A., Kukkonen, J., Ruuskanen, J. (2005) Evaluation of an integrated modelling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations. Atmospheric Environment 39(35), 6524-6536.

26.

Orr, M.J.L. (1996) Introduction to radial basis function networks, University of Edinbergh, EH89LW.

27.

Owega, S., Khan, B.U.Z., Evans, G.J., Jervis, R.E., Fila, M. (2006) Identification of long-range aerosol transport patterns to Toronto via classification of back trajectories by cluster analysis and neural network techniques. Chemo Metrics and Intelligent Laboratory Systems 83(1), 26-33.

28.

Romanof, N. (1982), A Markov chain model for the mean daily $SO_2$ concentrations. Atmospheric Environment 16(8), 1895-1897.

29.

Rumelhart, D.E., McClelland, J.L. (1986) Parallel distribution processing: Exploration in the microstructure of cognition, Cambridge, MA: MIT Press.

30.

Shamshad, A., Bawadi, M.A., Wan Hussin, W.M.A., Majid, T.A., Sanusi, S.A.M. (2005) First and second order Markov chain models for synthetic generation of wind speed time series. Energy 30, 693-708.

31.

Slaughter, J.C., Lumley, T., Sheppard, L., Koenig, J.Q., Shapiro, G.G. (2003) Effects of ambient air pollution on symptom severity and medication use in children with asthma. Annals of Allergy, Asthma and Immunology 91(4), 346-353.

32.

Slini, T., Kaprara, A., Karatzas, K., Moussiopoulos, N. (2006) $PM_{10}$ forecasting for Thessaloniki, Greece. Environ. Modell. Softw. 21, 559-565.

33.

Song, X.M. (1996) Radial basis function networks for empirical modeling of chemical process. MSc thesis, University of Helsinki.

34.

Sun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., Liu, S. (2013) Prediction of 24-hour-average $PM_{2.5}$ concentrations using a hidden Markov model with different emission distributions in Northern California. Science of the Total Environment 443, 93-103.

35.

Taylor, H., Karlin, S. (1998) An Introduction to Stochastic Modeling. Academic Press, San Diego, California.

36.

Voukantsis, D., Karatzas, K., Kukkonen, J., Rasanen, T., Karppinen, A., Kolehmainen, M. (2011) Intercomparison of air quality data using principal component analysis, and forecasting of $PM_{10}$ and $PM_{2.5}$ concentrations using artificial neural networks, in Thessaloniki and Helsinki. Science of the Total Environment 409, 1266-1276.

37.

Wang, X., Liu, W. (2012) Research on Air Traffic Control Automatic System Software Reliability Based on Markov Chain. Physics Procedia 24, 1601-1606.

38.

Wilks, D.S. (2006) Statistical methods in the atmospheric sciences. 2nd ed. Academic Press, xvii, 627 p.

39.

Zickus, M., Greig, A.J., Niranjan, M. (2002) Comparison of four machine learning methods for predicting $PM_{10}$ concentration in Helsinki, Finland. Water, Air and Soil Pollution 2(5), 717-729.

40.

Zurada, J.M. (1992) Introduction to Artificial Neural Systems, PWS; Singapore, 195-196.