References
- ADB (2020). Climate risk country profile: Vietnam. Asian Development Bank, pp. 27.
- Bates, P. D., Horritt, M. S., & Fewtrell, T. J. (2010). A Simple Inertial Formulation of the Shallow Water Equations for Efficient Two-Dimensional Flood Inundation Modelling. Journal of Hydrology, 387(1-2), 33–45. https://doi.org/10.1016/j.jhydrol.2010.03.027
- Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., & Kirchner, J. W. (2019). The Relative Importance of Different Flood-Generating Mechanisms Across Europe. Water Resources Research, 55(6), 4582–4593. https://doi.org/10.1029/2019WR024841
- DHI (2011). MIKE 11: A Modelling System for Rivers and Channels - User Guide. Danish Hydraulic Institute, pp. 542. Online available:https://euroaquae.tucottbus.de/Semester3/LectureNotes/Module31/Rhine/Mike11/MIKE11_UserManual.pdf
- Duy, N. H. (2023). Daily Streamflow Forecasting by Machine Learning in Tra Khuc river in Vietnam. Vietnam Journal of Earth Sciences, 45(1), 82–97. https://doi.org/10.15625/2615-9783/17914
- Elrahman, S. I. M. A., & Ataalmanan, I. M. I. (2023). Determination of the Hydrological and Morphometric Characteristics Using GIS. Civil and Environmental Engineering, 19(1), 39-47. https://doi.org/10.2478/cee-2023-0004
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press, pp. 785. Online available: http://alvarestech.com/temp/deep/Deep%20Learning%20by%20Ian%20Goodfellow,%20Yoshua%20Bengio,%20Aaron%20Courville%20(z-lib.org).pdf
- Hakala, K., Addor, N., Teutschbein, C., Vis, M., Dakhlaoui, H., & Seibert, J. (2020). Hydrological Modeling for Climate Change Impact. Encyclopedia of Water: Science, Technology, and Society, Edited by Patricia A. Maurice, pp. 1–20. https://doi.org/10.1002/9781119300762.wsts0062
- Hanh, D. K., Truong, D. D., & Trinh, B. T. K. (2024). Application of Numerical Modeling and GIS for Simulating Inundation Under Dam Failure Scenarios. Civil and Environmental Engineering, 20(1), 233–254. https://doi.org/10.2478/cee-2024-0019
- Hanh, N. D., Giang, N. T., Hoa, L. X., Vinh, T. N., & Nguyen, H. D. (2024). Multi-Step-Ahead Prediction of Water Levels Using Machine Learning: A Comparative Analysis in the Vietnamese Mekong Delta. Vietnam Journal of Earth Sciences, 46(4), 468–488. https://doi.org/10.15625/2615-9783/21067
- Hu, C.; Wu, Q.; Li, H.; Jian, S.; Li, N.; & Lou, Z. (2018). Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation. Water, 10(11), 1543. https://doi.org/10.3390/w10111543
- IPCC (2021). Climate Change 2021: The Physical Science Basis. Cambridge University Press, 2021, pp. 2391.
- Jayapadma, J. M. M. U., Wickramaarachchi, T. N., Silva, G. H. A. C., Ishidaira, H., & Magome, J. (2018). Rainfall-Runoff Modelling Using MIKE 11 (NAM Model): A Case Study of GIN River Basin. Proceeding of the 6th International Symposium on Advances in Civil and Environmental Engineering Practices for Sustainable Development (ACEPS-2018), pp. 231–238.
- Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. Proceeding of the conference paper at the 3rd International Conference for Learning Representations, San Diego. https://doi.org/10.48550/arXiv.1412.6980.
- Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Hernegger, M. (2018). Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrology and Earth System Sciences, 22(11), 6005–6022. https://doi.org/10.5194/hess-22-6005-2018.
- Le, X. H., Ho, H. V., Lee, G., & Jung, S. (2019). Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water, 11(7), 1387. https://doi.org/10.3390/w11071387.
- Le, G. S., Tran, L. T., Ho, L. H., & Park, E. (2023). F28: A Novel Coupling Strategy For 1D/2D Hydraulic Models for Flood Risk Assessment of the Mekong Delta. Journal of Hydroinformatics, 25(6), 2369–2388. https://doi.org/10.2166/hydro.2023.108.
- Leščešen, I., Tanhapour, M., Pekárová, P., Miklánek, P., & Bajtek, Z. (2025). Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia). Water, 17(6), 907. https://doi.org/10.3390/w17060907.
- Linh, N. T. M., Tri, D. Q., Thai, H. T., & Don, N. C. (2018). Application of a Two-Dimensional Model for Flooding and Floodplain Simulation: Case Study in Tra Khuc-Song Ve River in Viet Nam. Lowland Technology International, 20(03), 367–378.
- Liu, D., Jiang, W., Mu, L., & Wang, S. (2020). Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River. IEEE Access, 8, 90069–90086. https://doi.org/10.1109/ACCESS.2020.2993874.
- Loi, N. K., Liem, N. D., Tu, L. H., Hong, N. T., Truong, C. D., Tram, V. N. Q., Nhat, T. T., Anh, T. N., & Jeong, J. (2019). Automated Procedure of Real-Time Flood Forecasting in Vu Gia – Thu Bon River Basin, Vietnam by Integrating SWAT and HEC-RAS Models. Journal of Water & Climate Change, 10(3), 535–545. https://doi.org/10.2166/wcc.2018.015.
- Madsen, M. (2000). Automatic Calibration of a Conceptual Rainfall–Runoff Model Using Multiple Objectives. Journal of Hydrology, 235(3–4), 276–288. https://doi.org/10.1016/S0022-1694(00)00279-1
- Nhan dan (2025). Online available: https://nhandan.vn/bao-dam-an-ninh-nguon-nuoc-luu-vuc-song-hong-song-thai-binh-post894722.html?utm_source=chatgpt.com (Assessed on 19/7/2025).
- Quang, N. H., Tuan, V. A., Hang, L. T. T., Hung, N. M., The, D. T., Dieu, D. T., Anh, N. D., & Hackney, C. R. (2020). Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin. Water, 12(1), 71. https://doi.org/10.3390/w12010071
- Sabzipour, B., Arsenaurlt, R., Troin, M., Martel, J. L., Brissette, F., Brunet, F., & Mai, J. (2023). Comparing a Long Short-Term
- Memory (LSTM) Neural Network with a Physically-Based Hydrological Model for Streamflow Forecasting Over a Canadian Catchment. Journal of Hydrology, 627(A), 130380. https://doi.org/10.1016/j.jhydrol.2023.130380
- Sahoo, B. B., Jha, R., Singh, A., Kumar, D. (2019). Long Short-Term Memory (LSTM) Recurrent Neural Network for Low-Flow Hydrological Time Series Forecasting. Acta Geophysica, 67(4), 1471–1481. https://doi.org/10.1007/s11600-019-00330-1
- Seibert, J., & Vis, M. J. P. (2012). Teaching Hydrological Modeling with a User-Friendly Catchment-Runoff-Model Software Package. Hydrology and Earth System Sciences, 16, 3315–3325. https://doi.org/10.5194/hess-16-3315-2012
- Shen, C. (2018). A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists. Water Resources Research, 54(11), 8558–8593. https://doi.org/10.1029/2018WR022643
- Sit, M., Demiray, B. Z., Xiang, Z., Ewing, G. J., Sermet, Y., & Demir, I. (2020). A comprehensive review of deep learning applications in hydrology and water resources. Water Science & Technology, 82(12), 2635–2670. https://doi.org/10.2166/wst.2020.369
- Shen, C. (2018). A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists. Water Resources Research, 54(11), 8558–8593. https://doi.org/10.1029/2018WR022643
- Tao, L., He, X., Li, J., &Yang, D. (2019). A multiscale long short-term memory model with attention mechanism for improving monthly precipitation prediction. Journal of Hydrology, 602, 126815. https://doi.org/10.1016/j.jhydrol.2021.126815
- Thai, T.H., & Tri, D.Q. (2019). Combination of Hydrologic and Hydraulic Modeling on Flood and Inundation Warning: Case Study at Tra Khuc-Ve River Basin in Vietnam. Vietnam Journal of Earth Sciences, 41(03), 240–251. https://doi.org/10.15625/0866-7187/41/3/13866
- Thai, T. H., Tri, D. Q., Anh, N. X., Hoa, V. V., Nguyen, H. V., Nhat, N. V., Tuyet, Q. T. T., Pham, H. T. T., Chung, P. H., Thang, V. V., & Thuc, T. D. (2023). Numerical Simulation of the Flood and Inundation Caused by Typhoon Noru Downstream from the Vu Gia-Thu Bon River Basin. Sustainability, 15(10), 8203. https://doi.org/10.3390/su15108203
- Thanh, N. T., Nhung, D. H., Thuy, N. T., Thuc, T. D., Thang, V. V., Tri, D. Q., & Doanh, V. V. (2025). Sensitive Analysis of WRF-Hydro’s Parameters for Multi-Peak Flood Flow: A Case Study in the Ve River Basin, Vietnam. Civil and Environmental Engineering, 21(1), 334–348. https://doi.org/10.2478/cee-2025-0026
- Tri, D. Q., Thai, T. H., & Vo, V. V. (2022). Bias-Correction Data of IFS Rainfall Forecasts for Hydrological and Hydraulic Models to Forecast Flood Events. Arabian Journal of Geosciences, 15, 1535. https://doi.org/10.1007/s12517-022-10801-3
- Trinh, X. M., & Molkenthin, F. (2020). Flood Risk Assessment in the Tra Bong River Catchment, Vietnam. In: Gourbesville, P., Caignaert, G. (eds) Advances in Hydroinformatics. Springer Water. Springer, Singapore, pp. 575–592. https://doi.org/10.1007/978-981-15-5436-0_45
- Truong, V., Hoang, T., & Truong, D. (2025). Deep Learning for Downstream Water Level Prediction in Complex Hydrology Systems: An LSTM Approach. Open Journal of Modern Hydrology, 15(2), 218–232. https://doi.org/10.4236/ojmh.2025.152014
- UNDRR (2020). The Human Cost of Disasters: An overview of the last 20 years. United Nations Office for Disaster Risk Reduction, pp. 28. Online available: https://www.preventionweb.net/files/74124_humancostofdisasters20002019reportu.pdf?startDownload=true
- Wang, W., Liu, J., Li, C., Liu, Y., & Yu, F. (2021). Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity. Remote Sensing, 13(4), 595. https://doi.org/10.3390/rs13040595
- Yuan, Z., Liu, J., Liu, Y., Zhang, Q., Li, Y., & Li, Z. (2022). A Two-Stage Modelling Method for Multi-Station Daily Water Level Prediction. Environmental Modelling & Software, 156, 105468. https://doi.org/10.1016/j.envsoft.2022.105468
- Yu, Q., Tolson, B. A., Shen, H., Han, M., Mai, J., & Lin, J. (2024). Enhancing Long Short-Term Memory (LSTM)-Based Streamflow Prediction with a Spatially Distributed Approach. Hydrology and Earth System Sciences, 28(9), 2107–2122. https://doi.org/10.5194/hess-28-2107-2024
- Zhang, Y., Zhou, Z., Van Griensven Thé, J., Yang, S. X., & Gharabaghi, B. (2023). Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing. Water, 15(22), 3982. https://doi.org/10.3390/w15223982