| 1 | 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. |
| 2 | Chen, Z, and Ho, P,-H., 2019, Global-connected network with generalized ReLU activation. Pattern Recognit. 96, 106961. |
| 3 | 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. |
| 4 | LeCun, y, Bottou, L., Bengio, y, Haffner, p., 1998, Gradient-based learning applied to document recognition. Proc, IEEE, 86, 2278-2324. DOI |
| 5 | Song, C, M., 2022, Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicabilrty, J. Hydrol. 605, 127324. |
| 6 | 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. DOI |
| 7 | Kingma, D., Ba, J., 2014, Adam: A Method for Stochastic Optimization. arXiv, arXiv:1412.6980. |
| 8 | 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. DOI |
 |
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