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Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory $(2)$
To predict the humidity, the RMSE is shown the lowest value of 5. 732 $(%)$, when using the long short-term memory model in summer in Mokpo in testing 2 (the average humidity predicted from the input layer with six input nodes).
Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory $(1)$
It is important to calculate and analyze temperature and humidity prediction accuracies among quantitative meteorological forecasting.
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