참고문헌
- Bottou, L., 2010, Large-scale machine learning with stochastic gradient descent. In Proceedings of the COMPST AT 2010 NEC Labs America, Princeton, NJ, USA, 22-27 August 2010.
- Chen, Z, and Ho, P,-H., 2019, Global-connected network with generalized ReLU activation. Pattern Recognit. 96, 106961.
- Kingma, D., Ba, J., 2014, Adam: A Method for Stochastic Optimization. arXiv, arXiv:1412.6980.
- Kratzert, F., Klotz, 0., Brenner, C, Schulz, K, Herrnegger, M., 2018, Rainfall-runoffmodelling using long short-term memory (LSTM) networks. Hydrol. Earth Syst. Sci, 22 (11), 6005 -6022.
- LeCun, y, Bottou, L., Bengio, y, Haffner, p., 1998, Gradient-based learning applied to document recognition. Proc, IEEE, 86, 2278-2324. https://doi.org/10.1109/5.726791
- Maier, H., Jain, A, Dandy, G., Sudheer, K. P., 2010, Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environ. Model. Softw. 25, 891-909. https://doi.org/10.1016/j.envsoft.2010.02.003
- Song, C, M., 2022, Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicabilrty, J. Hydrol. 605, 127324.
- Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., Chau, K. -W., 2019, An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J. Hydrol. 569, 387-408. https://doi.org/10.1016/j.jhydrol.2018.11.069