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

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Abstract

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 7 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 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 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 simulation basis for the necessity of exploring and developing a novel neural network evaluation method in the future.

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Acknowledgements

This is supported by a Research Grant of Pukyong National University (2021 year).

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Authors and Affiliations

  1. Department of Physics, Pukyong National University, Busan, 48513, South Korea

    Ki-Hong Shin & Kyungsik Kim

  2. DigiQuay Company Ltd., Seocho-Gu, Seoul, 06552, South Korea

    Jae-Won Jung

  3. National Institute of Meteorological Research, Korea Meteorological Administration, Seogwipo, 63568, South Korea

    Ki-Ho Chang & Woon-Seon Jung

  4. Department of Environmental Atmospheric Sciences, Pukyong National University, Busan, 48513, South Korea

    Dong-In Lee & Cheol-Hwan You

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  1. Ki-Hong Shin
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Appendix A: Testings 1 and 2

Appendix A: Testings 1 and 2

As the result of Ref. [103], testing 1 has the four nodes Tt−1, Tt, Ht−1, Ht, in the input layer and the one output node Tt+1in output layer. Testing 2 has the four input nodes Tt−1, Tt, Ht−1, Ht, and the one output node Ht+1. It is not known beforehand what values of learning rates are appropriate. However, we select the five learning rate lr = 0.1, 0.2, 0.3, 0.4, and 0.5 for the ANN and the DNN, while the learning rate values for LSTM and LSTM-PC are lr = 0.001, 0.003, 0.005, 0.007, and 0.009, for different training sizes over three runs, 2500, 5000, and 7500 epochs. Particularly, the predicted accuracies of ELM are also obtained by averaging the results over 2500, 5000, and 7500 epochs. Tables 5 and 6 (the same as Tables 1 and 2 in Ref. [103]) are, respectively, the result of the computer simulation performed for testings 1 and 2.

Table 5 Values of RMSE, MAPE, MAE, and Theil’s-U (unit: %) of ten metropolitan cities in four seasons in testing 1, where lr and L-PC denote the learning rate and the LSTM-PC, respectively
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Table 6 Values of RMSE, MAPE, MAE, and Theil’s-U (unit: %) of ten metropolitan cities in four seasons in testing 2, where lr and L-PC denote the learning rate and the LSTM-PC, respectively
Full size table

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Shin, KH., Jung, JW., Chang, KH. et al. Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (ΙΙ). J. Korean Phys. Soc. 80, 1081–1097 (2022). https://doi.org/10.1007/s40042-022-00472-4

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