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

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)
Publication Information
Journal of Environmental Impact Assessment / v.12, no.4, 2003 , pp. 271-279 More about this Journal
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.
Keywords
Artificial neural networks; back propagation algorithm; sensitivity analysis; dispersion model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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