Environmental Engineering Research

Korean Society of Environmental Engineers (대한환경공학회)
권호 표지
DOI QR코드

DOI QR Code

Comparison between the Application Results of NNM and a GIS-based Decision Support System for Prediction of Ground Level SO2 Concentration in a Coastal Area

  • Published : 2009.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

A prototype GIS-based decision support system (DSS) was developed by using a database management system (DBMS), a model management system (MMS), a knowledge-based system (KBS), a graphical user interface (GUI), and a geographical information system (GIS). The method of selecting a dispersion model or a modeling scheme, originally devised by Park and Seok, was developed using our GIS-based DSS. The performances of candidate models or modeling schemes were evaluated by using a single index(statistical score) derived by applying fuzzy inference to statistical measures between the measured and predicted concentrations. The fumigation dispersion model performed better than the models such as industrial source complex short term model(ISCST) and atmospheric dispersion model system(ADMS) for the prediction of the ground level $SO_2$ (1 hr) concentration in a coastal area. However, its coincidence level between actual and calculated values was poor. The neural network models were found to improve the accuracy of predicted ground level $SO_2$ concentration significantly, compared to the fumigation models. The GIS-based DSS may serve as a useful tool for selecting the best prediction model, even for complex terrains.

Keywords

References

  1. Park, O. H. and Seok, M. G., "Selection of an appropriate model to predict plume dispersion in coastal areas," Atmos. Environ., 41, 6095-6101 (2007) https://doi.org/10.1016/j.atmosenv.2007.04.010
  2. Venkatesan, R., Mathiyarasu, R., and Somayaji, K. M., "A study of atmospheric dispersion of radionuclides at a coastal site using a modified Gaussian model and a mesoscale sea breeze model," Atmos. Environ., 36, 2933-2942 (2002) https://doi.org/10.1016/S1352-2310(02)00258-3
  3. Lyon, W. Q. and Cole, J. S., "Fumigation and plume trapping on the shores of Lake Michigan during stable onshore flow," J. App. Meteorology, 12, 494-510 (1973) https://doi.org/10.1175/1520-0450(1973)012<0494:FAPTOT>2.0.CO;2
  4. Turner, D. B., Workbook of Atmospheric Dispersion Estimates, 2nd ed., CRC Press, NC (1970)
  5. van Dop, H., Steenkist, R., and Nieuwstadt, F. T., "Revised estimates for continuous shoreline fumigation," American Meteorological Society, 18, 133-137 (1979)
  6. Engleman, I. R., Air Pollution Meteorology, Thmedia Publishing Company, KS, 79-90 (1996)
  7. Montgomery, T. L., Noriss, W. B., Thomas, F. W., and Carpenter, S. B., "A simplified technique used to evaluate atmospheric dispersion of emissions from large power plants,"J. Air Pollut. Control Assoc., 23, 388-394 (1973) https://doi.org/10.1080/00022470.1973.10469784
  8. Misra, P. K., "Dispersion from tall stack into a shoreline environment," Atmos. Environ., 14, 494-510 (1980) https://doi.org/10.1016/0004-6981(80)90203-6
  9. Okamoto, H., Ohba, R., and Kinoshita, S., "Prediction of diffusion taking account of topographical effects," Proc. 7th World Ctean Air Congress, Sydney, Australia, 34-41 (1986)
  10. Hanna, S. R., Briggs, G. A., Deardorff, J., Egan, B. A., Gif-ford, F. A., and Pasquill, F., "Meeting review - AMS workshop on stability classification scheme and sigma curve-summary of recommendations," Bull. Am. Meteorological Society, 58, 1305-1309 (1997)
  11. Angell, J. K. and Pack, D. J., "Atmosphehc lateral diffusion estimates from tetroons," Appl. Meteorology, 4, 418-425 (1965) https://doi.org/10.1175/1520-0450(1965)004<0418:ALDEFT>2.0.CO;2
  12. Sin, J. Y., "Prediction of Short Term Average $SO_2$ Concentration in Coastal Area Using Neural Network Models," ME Thesis, Pusan National University (2006)
  13. Pratap, R., Getting Started with MATLAB, Oxford University Press, NC (2004)
  14. Park, O. H., Sin, J. Y., and Seok, M. G., "Variation of ANN model's predictive performance concerning short-term (<24 hrs) $SO_2$ concentrations with prediction lagging time," J. KOSAE, 24(E2), 63-73 (2008) https://doi.org/10.5572/KOSAE.2008.24.1.063
  15. Chang, J. C. and Hanna, S. R., "Air quality model performance evaluation," Meteorology and Atmos. Phys., 87, 167-196 (2004) https://doi.org/10.1007/s00703-003-0070-7
  16. Zawar-Reza, P., Kingham, S., and Pearce, J., "Evaluation of a year-long dispersion modeling of PM10 using the mesoscale model TAPM for Christchurch, New Zealand," Sci. Total Environ., 349, 249-259 (2005) https://doi.org/10.1016/j.scitotenv.2005.01.037
  17. Ziomas, I. C., Tzoumaka, P., and Balis, D., "Ozone episodes in Athens, Greece. A modeling approach using data from the medcaphot-trace,"Atmos. Environ., 32, 2313-2321 (1998) https://doi.org/10.1016/S1352-2310(97)00414-7