1. Nhu, O., Thuy, N., Wilderspin, I., & Coulier, M. (2011). A preliminary analysis of flood and storm disaster data in Viet Nam. Ha Noi, March, 1–10. http://www.desinventar.net/doc/Viet_Nam_2011.pdf
2. Jain, S.K.; Mani, P.; Jain, S.K.; Prakash, P.; Singh, V.P.; Tullos, D.; Kumar, S.; Agarwal, S.P.; Dimri, A.P. A Brief review of flood forecasting techniques and their applications. Int. J. River Basin Manage. 2018, 16, 329–344. https://doi.org/10.1080/15715124.2017.1411920
3. Snieder, E.; Shakir, R.; Khan, U.T. A comprehensive comparison of four input variable selection methods for artificial neural network flow forecasting models. J. Hydrol. 2019, 124299. https://doi.org/10.1016/j.jhydrol.2019.124299.
4. Cloke, H.L.; Pappenberger, F. Ensemble flood forecasting: A review. J. Hydrol. 2009, 375, 613–626. https://doi.org/10.1016/j.jhydrol.2009.06.005
5. World Meteorological Organization (WMO). Manual on flood forecasting and warning. In World Meteorological Organization (Zenbakia 1072), 2011.
6. Ranit, A.B.; Durge, P.V. Different Techniques of Flood Forecasting and Their Applications. 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), 2018, 1–3. https://doi.org/10.1109/RICE.2018.8509058.
7. MAXWELL, A. Limitations of the use of the multiple linear regression model. Br. J. Math. Stat. Psychol. 2011, 28, 51–62. https://doi.org/10.1111/j.2044–8317.1975.tb00547.x.
8. Morss, R.E.; Wilhelmi, O.V.; Downton, M.W.; Gruntfest, E. Flood risk, uncertainty, and scientific information for decision making: Lessons from an interdisciplinary project. Bull. Am. Meteorol. Soc. 2005, 86, 1593–1601. https://doi.org/10.1175/BAMS–86–11–1593.
9. Madsen, H.; Skotner, C. Adaptive state updating in real–time river flow forecasting—a combined filtering and error forecasting procedure. J. Hydrol. 2005, 308, 302–312. https://doi.org/https://doi.org/10.1016/j.jhydrol.2004.10.030.
10. Götzinger, J.; Bárdossy, A. Generic error model for calibration and uncertainty estimation of hydrological models. Water Resour. Res. 2008, 44, 1–18. https://doi.org/10.1029/2007wr006691.
11. Vrugt, J.A.; Gupta, H.V.; Bouten, W., Sorooshian, S. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res. 2003, 39, 1201. https://doi.org/10.1029/2002WR001642.
12. Vu, T.H. Machine Learning Cơ bản. In Journal of Chemical Information and Modeling (Libk. 53, Zenbakia 9), 2013. https://doi.org/10.1017/CBO9781107415324.004.
13. Brownlee, J. Master Machine Learning Algorithms, 2020.
14. Erichson, N.B.; Mathelin, L.; Yao, Z.; Brunton, S.L.; Mahoney, M.W.; Kutz, J.N. Shallow neural networks for fluid flow reconstruction with limited sensors. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020, 476, 2238. https://doi.org/10.1098/rspa.2020.0097rspa20200097.
15. Zeeshan. (d.g.). Cost, Activation, Loss Function Neural Network Deep Learning. What are these? https://medium.com/@zeeshanmulla/cost-activation-loss-function-neural-network-deep-learning-what-are-these-91167825a4de
16. Tổng cục Khí tượng Thủy văn. QĐ 722/QĐ–TCKTTV về quy định sai số cho phép tại các vị trí dự báo thủy văn trên các sông thuộc phạm vi cả nước, 2018.