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


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.


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