  It is important to calculate and analyze temperature and humidity prediction
accuracies among quantitative meteorological forecasting. This study
manipulates the extant neural network methods to foster the predictive
accuracy. To achieve such tasks, we analyze and explore the predictive accuracy
and performance in the neural networks using two combined meteorological
factors (temperature and humidity). Simulated studies are performed by applying
the artificial neural network (ANN), deep neural network (DNN), extreme
learning machine (ELM), long short-term memory (LSTM), and long short-term
memory with peephole connections (LSTM-PC) machine learning methods, and the
accurate prediction value are compared to that obtained from each other
methods. Data are extracted from low frequency time-series of ten metropolitan
cities of South Korea from March 2014 to February 2020 to validate our
observations. To test the robustness of methods, the error of LSTM is found to
outperform that of the other four methods in predictive accuracy. Particularly,
as testing results, the temperature prediction of LSTM in summer in Tongyeong
has a root mean squared error (RMSE) value of 0.866 lower than that of other
neural network methods, while the mean absolute percentage error (MAPE) value
of LSTM for humidity prediction is 5.525 in summer in Mokpo, significantly
better than other metropolitan cities.
