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

  • Journal of Environmental Impact Assessment
  • Abbr : J EIA
  • 2003, 12(4), pp.271-280
  • Publisher : Korean Society Of Environmental Impact Assessment
  • Research Area : Engineering > Environmental Engineering

JEONG, HYOJOON 1 황원태 2 김은한 2 한문희 2 SUH KYUNG SUK 1

1한국원자력연구원
2한국원자력연구소

Candidate

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.

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