Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamical prediction of two meteorological factors using the deep neural
  network and the long short-term memory $(2)$   This paper presents the predictive accuracy using two-variate meteorological
factors, average temperature and average humidity, in neural network
algorithms. We analyze result in five learning architectures such as the
traditional artificial neural network, deep neural network, and extreme
learning machine, long short-term memory, and long-short-term memory with
peephole connections, after manipulating the computer-simulation. Our neural
network 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 from
outputs in ten metropolitan cities (Seoul, Daejeon, Daegu, Busan, Incheon,
Gwangju, Pohang, Mokpo, Tongyeong, and Jeonju). The error statistics is found
from the result of outputs, and we compare these values to each other after the
manipulation of five neural networks. As using the long-short-term memory model
in testing 1 (the average temperature predicted from the input layer with six
input nodes), Tonyeong has the lowest root mean squared error (RMSE) value of
0.866 $(%)$ in summer from the computer-simulation in order to predict the
temperature. 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). Particularly, the long short-term memory model is is found to be more
accurate in forecasting daily levels than other neural network models in
temperature and humidity forecastings. Our result may provide a
computer-simuation basis for the necessity of exploring and develping a novel
neural network evaluation method in the future.

3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat