Advances in environmental research

  • 제4권2호
  • /
  • Pages.83-104
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  • 2015
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  • 2234-1722(pISSN)
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  • 2234-1730(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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Multicity Seasonal Air Quality Index Forecasting using Soft Computing Techniques

  • Tikhe, Shruti S. (Department of Civil Engineering, Sinhgad College of Engineering) ;
  • Khare, K.C. (Department of Civil Engineering Symbiosis Institute of Technology) ;
  • Londhe, S.N. (Department of Civil Engineering, Vishwakarma Institute of Information Technology)
  • 투고 : 2014.12.17
  • 심사 : 2015.06.08
  • 발행 : 2015.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

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참고문헌

  1. Bose, N.K. and Liang, P. (2000), Neural Network Fundamentals with Graphs, Algorithms and Applications, Tata McGraw-Hill Publication, Delhi, India.
  2. Brunelli, U., Piazza, U. and Pignato, L. (2007), "Two-day ahead prediction of daily maximum concentrations of $SO_2,\;O_3,\;PM_{10},\;NO_2$, CO in the urban area of Palermo, Italy", Atmosp. Environ., 41(14), 2967-2995. https://doi.org/10.1016/j.atmosenv.2006.12.013
  3. Chen, J.-L., Islam, S. and Biswas, P. (1998), "Nonlinear dynamics of hourly ozone Concentrations: nonparametric short term prediction", Atmosp. Environ., 32(11), 1839-1848. https://doi.org/10.1016/S1352-2310(97)00399-3
  4. Comrie, A.C. and Diem, J.E. (1999), "Climatology and forecast modeling of ambient carbon monoxide in Phoenix, Arizona", Atmosp. Environ., 33(30), 5023-5036. https://doi.org/10.1016/S1352-2310(99)00314-3
  5. Davis, J.M. and Speckman, P. (1999), "A model for predicting maximum and 8 h average ozone in Houston", Atmosp. Environ., 33(16), 2487-2500. https://doi.org/10.1016/S1352-2310(98)00320-3
  6. Dilmaghani, S. (2007), "Spectral analysis of air quality data", Dissertation Report; University of Southern California, CA, USA.
  7. Draxler, R.R. (2000), "Meteorological factors of ozone predictability at Houston, Texas", J. Air Waste Manag. Assoc., 50(2), 259-271. https://doi.org/10.1080/10473289.2000.10463999
  8. EPA, (1999), Air Quality Index Reporting; Final Rule, Federal Register, Part III, 40 CFR Part 58.
  9. Gardner, M.W. and Dorling, S.R. (2000), "Artificial neural networks: The multilayer perceptron: A review of applications in the atmospheric sciences", Atmosp. Environ., 32(14-15), 2627-2636. https://doi.org/10.1016/S1352-2310(97)00447-0
  10. Grivas, G. and Chaloulakou, A. (2005), "Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece", Atmosp. Environ., 40(7), 216-1229.
  11. Hajek, P. and Olej, V. (2009), "Air quality indices and their modelling by hierarchical fuzzy inference systems", WSEAS Transactions on Environment and Development, 10(5), 661‐672.
  12. Hubbard, M. and Cobourn, W.G. (1998), "Development of a regression model to forecast ground-level ozone concentration in Louisville, KY", Atmosp. Environ., 32(14-15), 2637-2647. https://doi.org/10.1016/S1352-2310(97)00444-5
  13. Jiang, D., Zhang, Y., Hu, X., Zeng, Y. and Tan, J. and Shao, D. (2004), "Progress in developing an ANN model for air pollution index forecast", Atmosp. Environ., 38(40), 7055-7064. https://doi.org/10.1016/j.atmosenv.2003.10.066
  14. Kassomenos, P.A., Kelessis, A., Petrakakis, M., Zoumakis, N., Christidis Th. and Paschalidou, A.K. (2012), "Air quality assessment in a heavily polluted urban Mediterranean environment through air quality indices", Ecol. Indicators, 18, 259-268. https://doi.org/10.1016/j.ecolind.2011.11.021
  15. Khare, M. and Nagendra, S.A. (2007), "Artificial neural networks in vehicular pollution modelling", J. Stud. Comput. Intell., 41-45.
  16. Koza, J.R. (1992), Genetic Programming on the Programming of Computers by Means of Natural Selection, A Bradford Book, MIT Press.
  17. Kumar, A. and Goyal, P. (2011), "Forecasting of air quality in Delhi using Principal component regression technique", Atmosp. Pollut. Res., 2, 436-444. https://doi.org/10.5094/APR.2011.050
  18. Kyrkilis, G., Chaloulakou, A. and Kassomenos, P.A. (2007), "Development of an aggregate Air Quality Index for an urban Mediterranean agglomeration: Relation to potential health effects", Environ. Int., 33(5), 670-676. https://doi.org/10.1016/j.envint.2007.01.010
  19. Mayer, H., Jutta, H., Dirk, S. and Dieter, A. (2008), "Evolution of the air pollution in SW Germany evaluated by the long-term air quality index LAQx", Atmosp. Environ., 42(20), 5071-5078. https://doi.org/10.1016/j.atmosenv.2008.02.020
  20. Milanchus, M.L., Rao, T. and Zurbenko, I.G. (1998), "Evaluating the effectiveness of ozone Management efforts in the presence of meteorological variability", J. Air Waste Manag. Assoc., 48(3), 201-215. https://doi.org/10.1080/10473289.1998.10463673
  21. Mohan, M. and Anurag, K. (2007), "An Analysis of Annual and Seasonal Trends of Air Quality Index of Delhi", Environ. Monitor. Assess., 131(1-3), 267-277. https://doi.org/10.1007/s10661-006-9474-4
  22. Murthy Padmanabha, B. (2009), Environmental Meteorology, I.K. International Publishing House Pvt. Ltd., New Delhi, India.
  23. Perez, P. and Reyes, J. (2006), "An integrated neural network model for PM10 forecasting", Atmosp. Environ., 40(16), 2845-2851. https://doi.org/10.1016/j.atmosenv.2006.01.010
  24. Pires, J.C.M., Alvim- Ferraz, M.C.M., Pariera, M.C. and Martins, F.G. (2011), "Prediction of troposphere ozone concentration: Application of a methodology based on Darwin's Theory of Evolution", Expert Syst. Appl., 38(3), 1903-1908. https://doi.org/10.1016/j.eswa.2010.07.122
  25. Pun, B.K., Louis, J.F., Pai, P., Seigneur, C., Altshuler, S. and Franco, G. (2000), "Ozone formation in California's San Joaquin Valley: A critical assessment of modeling and data needs", J. Air Waste Manag. Assoc., 50(6), 961-971. https://doi.org/10.1080/10473289.2000.10464140
  26. Raga, G.B. and Le Moyne, L. (1999), "On the nature of air pollution dynamics in Mexico City - I. Nonlinear analysis", Atmosp. Environ., 30(23), 3987-3993. https://doi.org/10.1016/1352-2310(96)00122-7
  27. Rao, C.V.C., Chelani, A.B., Phadke, K.M. and Hasan, M.Z. (2002), "Formation of an air quality index in India", Int. J. Environ. Stud., 59(3), 331-342. https://doi.org/10.1080/00207230211300
  28. Roth, P.M. (1999), "A qualitative approach to evaluating the anticipated reliability of a photochemical air quality simulation model for a selected application", J. Air Waste Manag. Assoc., 49(9), 1050-1059. https://doi.org/10.1080/10473289.1999.10463895
  29. Russell, A. and Dennis, R. (2000), "NARSTO critical review of photochemical models and Modeling", Atmosp. Environ., 34(12-14), 2283-2324. https://doi.org/10.1016/S1352-2310(99)00468-9
  30. Salcedo, R.L.R., Alvim, M.C.M., Alves, C.A. and Martins, F.G. (1999), "Time-series analysis of air pollution data", Atmosp. Environ., 33(15), 2361-2372. https://doi.org/10.1016/S1352-2310(99)80001-6
  31. Sebald, L., Treffeisen, R., Reimery, E. and Hies, T. (2000), "Spectral analysis of air pollutants. Part 2: Ozone time series", Atmosp. Environ., 34(21), 3503-3509. https://doi.org/10.1016/S1352-2310(00)00147-3
  32. Sharma, M., Pandey, R., Maheshwari, M., Sengupta, B., Shukla, B.P., Gupta, N.K. and Johri S. (2003), "Interpretation of air quality data using an air quality index for the city of Kanpur, India", J. Environ. Eng. Sci., 2(6), 453-462. https://doi.org/10.1139/s03-047
  33. Thomas, S. and Jacko, R.B. (2007), "Model for forecasting expressway fine particulate matter and carbon monoxide concentration: Application of regression and neural network model", J. Air Waste Manag. Assoc., 57(4), 480-488. https://doi.org/10.3155/1047-3289.57.4.480
  34. Thompson, M.L., Reynolds, J., Cox, L.H.M, Guttorp, P. and Sampson, P.D. (2001), "A review of statistical methods for the meteorological adjustment of tropospheric ozone", Atmosp. Environ., 35(3), 617-630. https://doi.org/10.1016/S1352-2310(00)00261-2
  35. Tikhe Shruti, S., Khare, K.C. and Londhe, S.N. (2013), "Forecasting criteria air pollutants using data driven approaches: An Indian case study", IOSR J. Environ. Sci., Toxicol. Food Technol., 3(5), 01-08.
  36. URL: http://en.wikipedia.org/wiki/Mumbai (Accessed on February 2014)
  37. URL: http://safar.tropmet.res.in (Accessed on February 2014)
  38. URL: http://www.dnaindia.com (Accessed on February 2014)
  39. URL: http://www.hoparoundindia.com (Accessed on February 2014)
  40. URL: http://www.imdpune.gov.in (Accessed on February 2014)
  41. URL: http://www.mpcb.gov.in (Accessed on February 2014)
  42. URL: http://www.xlstat.com (Accessed on February 2014)
  43. Van den Elshout, S., Leger, K. and Nussio, F. (2008), "Comparing urban air quality in Europe in real time ‐ a review of existing air quality indices and the proposal of a common alternative", Environ. Int., 34(5), 720‐726.
  44. Wong, T.W., Tam, W.W.S., Yu, I.T.S., Lau, A.K.H., Pang, S.W. and Wong, A.H.S. (2013), "Developing a risk based air quality health index original research article", Atmosp. Environ., 76, 52-58. https://doi.org/10.1016/j.atmosenv.2012.06.071
  45. Zadeh, L. (1994), Fuzzy Logic, Neural Networks and Soft Computing, Communications of the ACM, 37(3), 77-84. https://doi.org/10.1145/175247.175255

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