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This paper presents the predictive accuracy using two-variate meteorologicalfactors, average temperature and average humidity, in neural networkalgorithms. We analyze result in five learning architectures such as thetraditional artificial neural network, deep neural network, and extremelearning machine, long short-term memory, and long-short-term memory withpeephole connections, after manipulating the computer-simulation. Our neuralnetwork modes are trained on the daily time-series dataset during seven years(from 2014 to 2020). From the trained results for 2500, 5000, and 7500 epochs,we obtain the predicted accuracies of the meteorological factors produced fromoutputs in ten metropolitan cities (Seoul, Daejeon, Daegu, Busan, Incheon,Gwangju, Pohang, Mokpo, Tongyeong, and Jeonju). The error statistics is foundfrom the result of outputs, and we compare these values to each other after themanipulation of five neural networks. As using the long-short-term memory modelin testing 1 (the average temperature predicted from the input layer with sixinput nodes), Tonyeong has the lowest root mean squared error (RMSE) value of0.866 $(%)$ in summer from the computer-simulation in order to predict thetemperature. To predict the humidity, the RMSE is shown the lowest value of5.732 $(%)$, when using the long short-term memory model in summer in Mokpo intesting 2 (the average humidity predicted from the input layer with six inputnodes). Particularly, the long short-term memory model is is found to be moreaccurate in forecasting daily levels than other neural network models intemperature and humidity forecastings. Our result may provide acomputer-simuation basis for the necessity of exploring and develping a novelneural network evaluation method in the future.

Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory $(2)$