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Artificial Intelligence Research on Visibility Forecast

  • Chao Xie
  • Xuekuan MaEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

The meteorological data in 2000 to 2017 from the China observation meteorological stations were collected for research. The multiple time scales variation characteristics and relations between visibility and meteorological elements were studied to summarize the weather conditions of low visibility weathers. The selecting factors which related to visibility and its change were input into an artificial neural network model for training. The long-term and meticulous visibility forecast of observation stations in China were calculated through the European Centre for Medium-Range Weather Forecasts (ECMWF) data. The error and TS score detection showed that the model had better reference than the China Meteorological Administration Unified Atmospheric Chemistry Environment model (CUACE) in the first half year of 2018.

Keywords

Low visibility Neural network Model release 

References

  1. 1.
    Fu, H., Chen, J.: Formation, features and controlling strategies of severe haze-fog pollutions in China. Sci. Total Environ. 578, 121 (2016)CrossRefGoogle Scholar
  2. 2.
    Gao, Z.K., Cai, Q., Yang, Y.X., et al.: Time-dependent limited penetrable visibility graph analysis of nonstationary time series. Physica A 476, 43–48 (2017)CrossRefGoogle Scholar
  3. 3.
    Cao, W.H., Liang, X.D., Li, Q.C.: A study of the stageful characteristics and influencing factors of a long-lasting fog/haze event in Beijing. Acta Meteorologica Sinica 71(5), 940–951 (2013)Google Scholar
  4. 4.
    Basahel, A., Rafiqul, I.M., Suriza, A.Z., et al.: Availability analysis of free-space-optical links based on rain rate and visibility statistics from tropical a climate. Optik-Int. J. Light Electron Opt. 127(22), 10316–10321 (2016)CrossRefGoogle Scholar
  5. 5.
    Pierini, J.O., Lovallo, M., Telesca, L.: Visibility graph analysis of wind speed records measured in central Argentina. Physica A 391(20), 5041–5048 (2012)CrossRefGoogle Scholar
  6. 6.
    Kartha, M.J.: Surface morphology of ballistic deposition with patchy particles and visibility graph. Phys. Lett. A 381(5), 556–560 (2017)CrossRefGoogle Scholar
  7. 7.
    Deng, H., Tan, H., Li, F., et al.: Impact of relative humidity on visibility degradation during a haze event: a case study. Sci. Total Environ. 569, 1149–1158 (2016)CrossRefGoogle Scholar
  8. 8.
    Cheung, H.C., Tao, W., Baumann, K., et al.: Influence of regional pollution outflow on the concentrations of fine particulate matter and visibility in the coastal area of southern China. Atmos. Environ. 39(34), 6463–6474 (2005)CrossRefGoogle Scholar
  9. 9.
    Li, Y., Huang, H.X.H., Griffith, S.M., et al.: Quantifying the relationship between visibility degradation and PM 2.5 constituents at a suburban site in Hong Kong: differentiating contributions from hydrophilic and hydrophobic organic compounds. Sci. Total Environ. 575, 1571–1581 (2017)CrossRefGoogle Scholar
  10. 10.
    Yu, X., Ma, J., An, J., et al.: Impacts of meteorological condition and aerosol chemical compositions on visibility impairment in Nanjing, China. J. Cleaner Prod. 131, 112–120 (2016)CrossRefGoogle Scholar
  11. 11.
    Lin, J., Li, J.: Spatio-temporal variability of aerosols over East China inferred by merged visibility-GEOS-Chem aerosol optical depth. Atmos. Environ. 132, 111–122 (2016)CrossRefGoogle Scholar
  12. 12.
    Kukkonen, J., Partanen, L., Karppinen, A., et al.: Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmos. Environ. 37(32), 4539–4550 (2003)CrossRefGoogle Scholar
  13. 13.
    Reich, S.L., Gomez, D.R., Dawidowski, L.E.: Artificial neural network for the identification of unknown air pollution sources. Atmos. Environ. 33(18), 3045–3052 (1999)CrossRefGoogle Scholar
  14. 14.
    Lou, W.G., Ting-Ting, Q.I., Lou, Y.Y., et al.: General regression neural network based on tax payment assessment and its empirical research. Syst. Eng. (2015)Google Scholar
  15. 15.
    Xue-Kuan, M.A., Cai, X.N., Yang, G.M.: Study on fog synoptic characteristics and fog forecast method in Chongqing. Clim. Environ. Res. 12(6), 795–803 (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Meteorological CenterBeijingChina

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