Application of Back-propagation Algorithm for the forecasting of Temperature and Humidity

온도 및 습도의 단기 예측에 있어서 역전파 알고리즘의 적용

  • Jeong, Hyo-Joon (Nuclear Environmental Research Division, Korea Atomic Energy Research Institute) ;
  • Hwang, Won-Tae (Nuclear Environmental Research Division, Korea Atomic Energy Research Institute) ;
  • Suh, Kyung-Suk (Nuclear Environmental Research Division, Korea Atomic Energy Research Institute) ;
  • Kim, Eun-Han (Nuclear Environmental Research Division, Korea Atomic Energy Research Institute) ;
  • Han, Moon-Hee (Nuclear Environmental Research Division, Korea Atomic Energy Research Institute)
  • 정효준 (한국원자력연구소 환경연구부) ;
  • 황원태 (한국원자력연구소 환경연구부) ;
  • 서경석 (한국원자력연구소 환경연구부) ;
  • 김은한 (한국원자력연구소 환경연구부) ;
  • 한문희 (한국원자력연구소 환경연구부)
  • Received : 2003.05.09
  • Accepted : 2003.07.29
  • Published : 2003.09.30

Abstract

Temperature and humidity forecasting have been performed using artificial neural networks model(ANN). We composed ANN with multi-layer perceptron which is 2 input layers, 2 hidden layers and 1 output layer. Back propagation algorithm was used to train the ANN. 6 nodes and 12 nodes in the middle layers were appropriate to the temperature model for training. And 9 nodes and 6 nodes were also appropriate to the humidity model respectively. 90% of the all data was used learning set, and the extra 10% was used to model verification. In the case of temperature, average temperature before 15 minute and humidity at present constituted input layer, and temperature at present constituted out-layer and humidity model was vice versa. The sensitivity analysis revealed that previous value data contributed to forecasting target value than the other variable. Temperature was pseudo-linearly related to the previous 15 minute average value. We confirmed that ANN with multi-layer perceptron could support pollutant dispersion model by computing meterological data at real time.

References

  1. 김대수. 1999. 신경망이론과 응용(I). 하이테크정보. 92-93
  2. Balkin, S. D., Ord, J. K., 2000, Automatic neural network modelling for univariate time series, International Journal of Forecasting, 16, 509-515 https://doi.org/10.1016/S0169-2070(00)00072-8
  3. Hwarng, H. B., Ang, H. T., 2001, A simple neural network for ARMA(p, q) time series, The International Journal of Management Science, 29,319-333
  4. Maier, H. R., Dandy, G. c., Burch, M. D., 1998, Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia, Ecological Modelling, 105, 257-272 https://doi.org/10.1016/S0304-3800(97)00161-0
  5. Abdul-Wahab, S. A., Al-Alawi, S. M., 2002, Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks, Environmental Modelling & Software, 17(3), 219-228 https://doi.org/10.1016/S1364-8152(01)00077-9
  6. Kung, S. Y, 1993, Digital Neural Networks, Prentice Hall International Inc, 30-33
  7. Gardner, M. W., Dorling, S. R., 1999, Neural network modelling and prediction of hourly $NO_{x}$ and $NO_{2}$ concentrations in urban air in London, Atmospheric Environment, 33(5), 709-719 https://doi.org/10.1016/S1352-2310(98)00230-1
  8. 정효준. 조일형. 이홍근. 2002. 인공신경망을 이용한 $TiO_{2}$$H_{2}O_{2}$의 유기물 제거효율 평가. 대한환경공학회지. 24(10). 1785-1795
  9. Skapura, D. M., 1996, Building Neural Networks, Addison Wesley, 1-5
  10. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., 1994, Time Series Analysis, Forecasting and control, Prentice-Hall Inc. 19-45
  11. Kolehmainen, M., Martikainen, H., Ruuskanen, J.,2001, Neural networks and periodic components used in air quality forecasting, Atmospheric Environment, 35, 815-825 https://doi.org/10.1016/S1352-2310(00)00385-X
  12. Podnar, D., Korain, D., Panorska, A., 2002, Application of artificial neural networks to modeling the transport and dispersion of tracers in complex terrain, Atmospheric Environment, 36(3), 561-570 https://doi.org/10.1016/S1352-2310(01)00446-0