Bessar, M.A., Matte, P. & Anctil, F. (2020). Uncertainty analysis of a 1d river hydraulic model with adaptive calibration. Water, 12(2), 561. https:// doi.org/10.3390/w12020561.
Brunner, G.W. (2016). HEC-RAS river analysis system 2D modeling user’s manual. US Army Corps of Engineers—Hydrologic Engineering Center, 1-171.
Bühler, M.M., Sebald, C., Rechid, D., Baier, E., Michalski, A., Rothstein, B., ... & Buziek, G. (2021). Application of copernicus data for climate-relevant urban planning using the example of water, heat, and vegetation. Remote sensing, 13(18), 3634. https:// doi.org/10.3390/rs13183634.
Camacho, R.A., Zhang, Z. & Chao, X. (2019). Receiving water quality models for TMDL development and implementation. Journal of Hydrologic Engineering, 24(2), 04018063. https:// doi.org/10.1061/(ASCE)HE.1943-5584.0001723.
Chow, V.T. (1959). Open-channel hydraulics. McGraw-Hill.
Daramola, S., Muñoz, D.F., Muñoz, P., Saksena, S. & Irish, J. (2025). Predicting the evolution of extreme water levels with long short‐term memory station‐based approximated models and transfer learning techniques.
Water Resources Research,
61(3), e2024WR039054.
https://doi.org/10.1029/ 2024WR039054.
Di Baldassarre, G. & Montanari, A. (2009). Uncertainty in river discharge observations: a quantitative analysis. Hydrology and Earth System Sciences, 13(6), 913-921.
Ebrahimi, N., Gharibreza, M., Hosseini, M. & Ashraf, M.A. (2017). Experimental study on the impact of vegetation coverage on flow roughness coefficient and trapping of sediment. Geology, Ecology, and Landscapes, 1(3), 167-172.
Ezzati, S., Eydi, Z. & Mohajeri, S.H. (2025). Determination of the legal bed boundary of natural canals branching from rivers in floodplains using a hydraulic analysis approach (Case study: Shahrestan Canal). Journal of Hydraulics, 20(4), 123–136. (In Persian)
Farfán-Durán, J.F. & Cea, L. (2024). Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain. Earth Science Informatics, 17(6), 5289-5315.
Green, A.C., Lewis, E., Tong, X. & Wardle, R. (2025). A framework for incorporating rainfall data into a flooding digital twin.
Journal of Hydrology,
656, 132893.
https://doi.org/10.1016/j.jhydrol. 2025.132893.
HEC-RAS. (2016). River analysis system, hydraulic reference manual, USACE version 5.0. US Army Corps of Engineers, CPD-68.
Heydarnzhad, H. & Badiei, P. (2025). Evaluation of the effects of Anzali Port Development on Sedimentation at Anzali Wetland by Numerical Simulations. Journal of Hydraulics, 20(4), 57–70. (In Persian)
Jamal, P. & Valizadeh, D. (2019). Flood hazard zoning in the Iranshahr River using two-dimensional numerical modeling and GIS. Journal of Water and Soil Sciences, 23(4), 71–83. (In Persian)
Khajeh, S., Ataie-Ashtiani, B. & Hosseini, S.M. (2022). Effect of DEM resolution in flood modeling, a case study of Gorganrood River Northeastern Iran. Natural Hazards, 112(3), 2673-2693.
Li, X., Xu, W., Ren, M., Jiang, Y. & Fu, G. (2022). Hybrid CNN-LSTM models for river flow prediction. Water Supply, 22(5), 4902-4919.
Mihu-Pintilie, A., Cîmpianu, C.I., Stoleriu, C.C., Pérez, M.N. & Paveluc, L.E. (2019). Using high-density LiDAR data and 2D streamflow hydraulic modeling to improve urban flood hazard maps: A HEC-RAS multi-scenario approach. Water, 11(9), 1832. https://doi.org/10.3390/w11091832.
Pathan, A.I., Sidek, L.B.M., Basri, H.B., Hassan, M.Y., Khebir, M.I.A.B., Omar, S.M.B.A., ... & Ahmed, A.N. (2024). Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques.
Ain Shams Engineering Journal,
15(7), 102854.
https://doi.org/10.1016/ j.asej.2024.102854.
Rajabizadeh, Y., Ayyoubzadeh, S.A. & Zahiri, A. (2019). Flood Survey of Golestan Province in 2018-2019 and providing solutions for its control and management in the future. Journal of Ecohydrology, 6(4), 921-942.
Rezaei-Ghaleh, L., Rezaei, H. & Ghorbani, K. (2025). Effect of dredging on flood extent and depth in low-slope areas using two-dimensional simulation (Case study: Aq Qala County). Journal of Civil and Environmental Engineering, 55(118), 1–10. (In Persian)
Shawon, S.M., Haider, S.N., Chakma, A., Alam, M.W., Islam, M.T. & Rana, M.F. (2024). DeepFlowNet: Deep Learning Based Daily Water Flow Forecasting of Test River. In: 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON), 839-844, IEEE.
https://doi.org/ 10.1109/PEEIACON63629.2024.10800657.
Syed, T.A., Muhammad, M.A., AlShahrani, A.A., Hammad, M. & Naqash, M.T. (2024). Smart water management with digital twins and multimodal transformers: A predictive approach to usage and leakage detection. Water, 16(23), 3410. https:// doi.org/10.3390/w16233410.
Teymouri, R., Dehghani, A.A. & Meftah Helghi, M. (2025). Investigating the effect of parallel processing and different mesh sizes on the speed and accuracy of flood modeling using STE software. Scientific Journal of Hydraulics, 20(4), 1–21. (In Persian)
Thomas, V.S. (2020). Understanding the role of green infrastructure in climate change resiliency of transportation infrastructure, Doctoral dissertation, Kansas State University.
Truong, V.A., Hoang, T.N.M. & Truong, D.V. (2025). Deep learning for downstream water level prediction in complex hydrology systems: An LSTM approach. Open Journal of Modern Hydrology, 15(2), 218-232.
Van der Meer, J.W., Hardeman, B., Steendam, G.J., Schüttrumpf, H. & Verheij, H. (2010). Flow depths and velocities at crest and landward slope of a dike, in theory and with the wave overtopping simulator. In: J., Mc Kee Smith, & P., Lynett (Eds.), Proceedings of 32nd Conference on Coastal Engineering, ICCE 2010 (pp. 1-15). s.n..
Van, T., Bui, D.X., Do, T.A.T. & Do, A.N.T. (2024). Assessing flood susceptibility in Hanoi using machine learning and remote sensing: implications for urban health and resilience. Natural Hazards, 121(9), 10149-10170.
World Meteorological Organization (2010). Manual on Stream Gauging (WMO-No. 1044). Geneva, Switzerland: WMO.
Wu, W. & Wan, L. (2022). Coastal ecological and environmental management under multiple anthropogenic pressures: A review of theory and evaluation methods.
Current Trends in Estuarine and Coastal Dynamics, 385-415.
https://doi.org/ 10.1016/B978-0-443-21728-9.00013-2.
Zhang, D., Holland, E.S., Lindholm, G. & Ratnaweera, H. (2018). Enhancing operation of a sewage pumping station for inter catchment wastewater transfer by using deep learning and hydraulic model. arXiv preprint arXiv:1811.06367. https://doi.org/10.13140/RG.2.2.27769.72808.
Zheng, F., Zhang, Y., Li, X. & Wang, Z. (2024). A hybrid deep learning approach for streamflow prediction incorporating watershed memory and residual error correction. Hydrology Research, 55(4), 498–512.