References
- Chen, J., J. Yu, M. Song, and V. Valdmanis, 2019: Factor decomposition and prediction of solar energy consumption in the United States. J. Clean. Prod., 234, 1210-1220, doi:10.1016/j.jclepro.2019.06.173.
- Farzad, A., H. Mashayekhi, and H. Hassanpour, 2019: A comparative performance analysis of different activation functions in LSTM networks for classification. Neural Comput. Applic., 31, 2507-2521, doi:10.1007/s00521-017-3210-6.
- Feng, C., M. Cui, B.-M. Hodge, and J. Zhang, 2017: A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Appl. Energ., 190, 1245-1257, doi:10.1016/j.apenergy.2017.01.043.
- Gal, Y., and Z. Ghahramani, 2016: A theoretically grounded application of dropout in recurrent neural network. Preprints, 30th Conference on Neural Information Processing Systems, 14 pp [Available online at https://arxiv.org/abs/1512.05287].
- Hewage, P., M. Trovati, E. Pereira, and A. Behera, 2021: Deep learning-based effective fine-grained weather forecasting model. Pattern Anal. Applic., 24, 343-366, doi: 10.1007/s10044-020-00898-1.
- Holton, J., and G. Hakim, 2012: An Introduction to Dynamic Meteorology, 5th edition. Academic Press, 552 pp.
- Hochreiter, S., and J. Schmidhuber, 1997: Long short-term memory. Neural Comput., 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Hong, S.-Y., and M. Kanamitsu, 2014: Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations. Asia-Pac. J. Atmos. Sci., 50, 83-104, doi:10.1007/s13143-014-0029-2.
- Jozefowicz, R., W. Zaremba, and I. Sutskever, 2015: An empirical exploration of recurrent network architectures. Proc., The 32nd International conference on machine learning, 37, ICML, 2342-2350, doi:10.5555/3045118.3045367.
- Kiperwasser, E., and Y. Goldberg, 2016: Simple and accurate dependency parsing using bidirectional LSTM feature representations. T. Assoc. Comput. Linguist., 4, 313-327, doi:10.1162/tacl_a_00101.
- KMA, 2020: Validation of Numerical Prediction System (2019). Tech. Rep., Numerical Modeling Center, Korea Meteorological Administration, 11-1360709-000001-10, 297 pp (in Korean).
- Lea, C., M. D. Flynn, R. Vidal, A. Reiter, and G. D. Hager, 2017: Temporal convolutional networks for action segmentation and detection. Proc., 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers Inc., 1003-1012, doi:10.1109/CVPR.2017.113.
- Majhi, B., D. Naidu, A. P. Mishra, and S. C. Satapathy, 2020: Improved prediction of daily pan evaporation using Deep-LSTM model. Neural Comput. Applic., 32, 7823-7838, doi:10.1007/s00521-019-04127-7.
- Poornima, S., and M. Pushpalatha, 2019: Prediction of rainfall using intensified LSTM based recurrent neural network with weighted linear units. Atmosphere, 10, 668, doi:10.3390/atmos10110668.
- Rumelhart, D. E., G. E. Hinton, and R. J. Williams, 1986: Learning representations by back-propagating errors. Nature, 323, 533-536. https://doi.org/10.1038/323533a0
- Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, 2014: Dropout: a simple way to prevent neural networks from overfitting. J. Machine Learn. Res., 15, 1929-1958.
- Tieleman, T., and G. Hinton, 2012: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 26-31.
- Wang, H., and B. Raj, 2017: On the origin of deep learning. Preprint, arXiv, 72 pp [Available online at https://arxiv.org/abs/1702.07800].
- Yang, B., S. Sun, J. Li, X. Lin, and Y. Tian, 2019: Traffic flow prediction using LSTM with feature enhancement. Neurocomputing, 332, 320-327, doi:10.1016/j.neucom.2018.12.016.
- Zhang, Q., H. Wang, J. Dong, G. Zhong, and X. Sun, 2017: Prediction of sea surface temperature using long short-term memory. IEEE Geosci. Remote S., 14, 1745-1749, doi:10.1109/LGRS.2017.2733548.
- Zhang, T., S. Song, S. Li, L. Ma, S. Pan, and L. Han, 2019: Research on gas concentration prediction models based on LSTM multidimensional time series. Energies, 12, 161, doi:10.3390/en12010161.