Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H.: CNN variants for computer vision: History, architecture, application, challenges and future scope, Electronics, 10, 2470, https://doi.org/10.3390/electronics10202470, 2021.
Boothroyd, R. J., Williams, R. D., Hoey, T. B., Barrett, B., and Prasojo, O. A.: Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change, Wiley Interdisciplinary Reviews: Water, https://doi.org/10.1002/wat2.1496, 2021.
Brasington, J. and Richards, K.: Reduced-complexity, physically-based geomorphological modelling for catchment and river management, Geomorphology, 90, 171–177, https://doi.org/10.1016/j.geomorph.2006.10.028, 2007.
Chen, J., Wang, Z., Li, M., Wei, T., and Chen, Z.: Bedform characteristics during falling flood stage and morphodynamic interpretation of the middle–lower Changjiang (Yangtze) River channel, China, Geomorphology, 147, 18–26, 2012.
Choné, G., Biron, P. M., and Buffin-Bélanger, T.: Flood hazard mapping techniques with LiDAR in the absence of river bathymetry data, in: E3S Web of Conferences, 40, 6005, https://doi.org/10.1051/e3sconf/20184006005, 2018.
Chu, H., Wu, W., Wang, Q. J., Nathan, R., and Wei, J.: An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions, Environ. Model. Softw., 124, 104587, https://doi.org/10.1016/j.envsoft.2019.104587, 2020.
Costigan, K. H., Daniels, M. D., Perkin, J. S., and Gido, K. B.: Longitudinal variability in hydraulic geometry and substrate characteristics of a Great Plains sand-bed river, Geomorphology, 210, 48–58, 2014.
De Goede, E. D.: Historical overview of 2D and 3D hydrodynamic modelling of shallow water flows in the Netherlands, Ocean Dyn., 70, 521–539, 2020.
Deltares: Delft3D-FLOW. Simulation of Multi-Dimensional Hydrodynamic Flow and Transport Phenomena, Including Sediments: User Manual, Version 3.04, Deltares, Delft, the Netherlands,
https://oss.deltares.nl/web/delft3d/manuals (last access: 17 April 2026), 2010.
De Melo, C. M., Torralba, A., Guibas, L., DiCarlo, J., Chellappa, R., and Hodgins, J.: Next-generation deep learning based on simulators and synthetic data, Trends Cogn. Sci., 26, 174–187, 2022.
DHI: MIKE 21C, curvilinear model for river morphology, user guide, MIKE Powered by DHI,
https://www.dhigroup.com/upload/dhisoftwarearchive/shortdescriptions/wr/m21c_short_2004.pdf (last access: 17 April 2026), 2017.
Donati, D., Stead, D., Brideau, M.-A., and Ghirotti, M.: Using pre-failure and post-failure remote sensing data to constrain the three-dimensional numerical model of a large rock slope failure, Landslides, 18, 827–847, 2021.
Fang, Y. and Jawitz, J. W.: The evolution of human population distance to water in the USA from 1790 to 2010, Nat. Commun., 10, 1–8, 2019.
Fathi, M. M., Smith, V., Awadallah, A. G., Fernandes, A. M., Hren, M. T., and Terry, D. O.: Constructing Long-Term Hydrographs for River Climate-Resilience: A Novel Approach for Studying Centennial to Millennial River Behavior, Water Resour. Res., 60, e2024WR037666, https://doi.org/10.1029/2024WR037666, 2024.
Fathi, M. M., Al Mehedi, M. A., Smith, V., Fernandes, A. M., Hren, M. T., and Terry Jr., D. O.: Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths, Sci. Rep., 15, 15820, https://doi.org/10.1038/s41598-025-96577-4, 2025a.
Fathi, M. M., Liu, Z., Fernandes, A. M., Hren, M. T., Terry, D. O., Nataraj, C., and Smith, V.: Spatiotemporal Flood Depth and Velocity Dynamics Using a Convolutional Neural Network Within a Sequential Deep-Learning Framework, Environ. Model. Softw., 106307, https://doi.org/10.1016/j.envsoft.2024.106307, 2025b.
Gelfenbaum, G., Stevens, A. W., Miller, I., Warrick, J. A., Ogston, A. S., and Eidam, E.: Large-scale dam removal on the Elwha River, Washington, USA: Coastal geomorphic change, Geomorphology, 246, 649–668, 2015.
Ghose, D. K.: Sediment yield prediction using neural networks at a watershed in south east India, ISH J. Hydraul. Eng., 24, 230–238, 2018.
Giri, S., Omer, A., Shrestha, B., Kayastha, N., and Froehlich, D. C.: Significance of geomorphological processes for safety and sustainability of water infrastructures, in: 14th International Symposium on River Sedimentation, 16–19 September 2019, Chengdu, China, 2019.
Girshick, R.: Fast r-cnn, in: Proceedings of the IEEE international conference on computer vision, 1440–1448, https://doi.org/10.1109/ICCV.2015.169, 2015.
Gonzales-Inca, C., Calle, M., Croghan, D., Torabi Haghighi, A., Marttila, H., Silander, J., and Alho, P.: Geospatial artificial intelligence (GeoAI) in the integrated hydrological and fluvial systems modeling: Review of current applications and trends, Water, 14, 2211, https://doi.org/10.3390/w14142211, 2022.
Hosseiny, H., Masteller, C. C., Dale, J. E., and Phillips, C. B.: Development of a machine learning model for river bed load, Earth Surf. Dynam., 11, 681–693, https://doi.org/10.5194/esurf-11-681-2023, 2023.
Huang, C.-C., Chang, M.-J., Lin, G.-F., Wu, M.-C., and Wang, P.-H.: Real-time forecasting of suspended sediment concentrations in reservoirs by the optimal integration of multiple machine learning techniques, J. Hydrol. Reg. Stud., 34, 100804, https://doi.org/10.1016/j.ejrh.2021.100804, 2021.
Kabir, S., Patidar, S., Xia, X., Liang, Q., Neal, J., and Pender, G.: A deep convolutional neural network model for rapid prediction of fluvial flood inundation, J. Hydrol., 590, 125481, https://doi.org/10.1016/j.jhydrol.2020.125481, 2020.
Karim, F., Armin, M. A., Ahmedt-Aristizabal, D., Tychsen-Smith, L., and Petersson, L.: A review of hydrodynamic and machine learning approaches for flood inundation modeling, Water, 15, 566, https://doi.org/10.3390/w15030566, 2023.
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang, L.: Physics-informed machine learning, Nat. Rev. Phys., 3, 422–440, 2021.
Kasprak, A., Brasington, J., Hafen, K., Williams, R. D., and Wheaton, J. M.: Modelling braided river morphodynamics using a particle travel length framework, Earth Surf. Dynam., 7, 247–274, https://doi.org/10.5194/esurf-7-247-2019, 2019.
Kaveh, K., Kaveh, H., Bui, M. D., and Rutschmann, P.: Long short-term memory for predicting daily suspended sediment concentration, Eng. Comput., 37, 2013–2027, 2021.
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proc., 1, arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2015.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Kwon, S., Shin, J., Seo, I. W., Noh, H., Jung, S. H., and You, H.: Measurement of suspended sediment concentration in open channel flows based on hyperspectral imagery from UAVs, Adv. Water Resour., 159, 104076, https://doi.org/10.1016/j.advwatres.2021.104076, 2022.
Lesser, G. R., Roelvink, J. A. v, van Kester, J. A. T. M., and Stelling, G. S.: Development and validation of a three-dimensional morphological model, Coast. Eng., 51, 883–915, 2004.
Ma, M., Zhao, G., He, B., Li, Q., Dong, H., Wang, S., and Wang, Z.: XGBoost-based method for flash flood risk assessment, J. Hydrol., 598, 126382, https://doi.org/10.1016/j.jhydrol.2021.126382, 2021.
Mananoma, T.: The effect of sediment supply to the damage of infrastructures, Eff. Sediment Supply to Damage Infrastructures, 1–489,
http://repo.unsrat.ac.id/id/eprint/31 (last access: 17 April 2026), 2009.
McDonald, R. R., Nelson, J. M., Fosness, R. L., and Nelson, P. O.: Field scale test of multi-dimensional flow and morphodynamic simulations used for restoration design analysis, USGS Publications Warehouse,
https://pubs.usgs.gov/publication/70175622 (last access: 17 April 2026), 2016.
Mehedi, M. A. Al, Smith, V., Hosseiny, H., and Jiao, X.: Unraveling the complexities of urban fluvial flood hydraulics through AI, Sci. Rep., 12, 18738, https://doi.org/10.1038/s41598-022-23214-9, 2022.
Mentaschi, L., Besio, G., Cassola, F., and Mazzino, A.: Problems in RMSE-based wave model validations, Ocean Model., 72, 53–58, 2013.
Mohamad, T. H., Abbasi, A., Kim, E., and Nataraj, C.: Application of deep cnn-lstm network to gear fault diagnostics, in: 202
1 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–6, ISBN 978-1-6654-1970-3, 2021.
Morgan, J. A., Kumar, N., Horner-Devine, A. R., Ahrendt, S., Istanbullouglu, E., and Bandaragoda, C.: The use of a morphological acceleration factor in the simulation of large-scale fluvial morphodynamics, Geomorphology, 356, 107088, https://doi.org/10.1016/j.geomorph.2020.107088, 2020.
Ouellet-Proulx, S., St-Hilaire, A., Courtenay, S. C., and Haralampides, K. A.: Estimation of suspended sediment concentration in the Saint John River using rating curves and a machine learning approach, Hydrol. Sci. J., 61, 1847–1860, 2016.
PyTorch: ReduceLROnPlateau: PyTorch 2.3 documentation,
https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html (last access: May 2024), 2024.
Sahraei, S., Alizadeh, M. R., Talebbeydokhti, N., and Dehghani, M.: Bed material load estimation in channels using machine learning and meta-heuristic methods, J. Hydroinform., 20, 100–116, 2018.
Stigler, S. M.: The history of statistics: The measurement of uncertainty before 1900, Harvard University Press, ISBN 9780674256859, 1990.
Talukdar, S., Mankotia, S., Shamimuzzaman, M., Shahfahad, and Mahato, S.: Wetland-inundated area modeling and monitoring using supervised and machine learning classifiers, Adv. Remote Sens. Nat. Resour. Monit., 346–365, https://doi.org/10.1002/9781119616016.ch17, 2021.
USACE: HEC-RAS Two-Dimensional Sediment Transport Technical Reference Manual,
https://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS%202D%20Sediment%20Technical%20Reference%20Manual-v6.4.1.pdf (last access: 17 April 2026), 2021.
USGS: NED 1/3 arc-second n38w099 1
× 1 degree IMG 2018, U.S. Geological Survey,
https://apps.nationalmap.gov/downloader/ (last access: 17 April 2026), 2018.
Wheaton, J. M., Bouwes, N., Mchugh, P., Saunders, C., Bangen, S., Bailey, P., Nahorniak, M., Wall, E., and Jordan, C.: Upscaling site-scale ecohydraulic models to inform salmonid population-level life cycle modeling and restoration actions–Lessons from the Columbia River Basin, Earth Surf. Process. Landforms, 43, 21–44, 2018.
Wu, W. and Lin, Q.: Nonuniform sediment transport under non-breaking waves and currents, Coast. Eng., 90, 1–11, 2014.
Yu, Y., Si, X., Hu, C., and Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures, Neural Comput., 31, 1235–1270, 2019.
Zakipour, M., Yazdandoost, F., Alizad, K., Izadi, A., and Farhangmehr, A.: An Integrated Resilient Sediment Transport RIsk Management (IRSTRIM) Approach for Estuaries, J. Mar. Sci. Eng., 11, 1471, https://doi.org/10.3390/jmse11071471, 2023.
Zounemat-Kermani, M., Fadaee, M., Adarsh, S., and Hinkelmann, R.: Predicting Sediment transport in sewers using integrative harmony search-ANN model and factor analysis, in: IOP Conference Series: Earth and Environmental Science, 12004, https://doi.org/10.1088/1755-1315/491/1/012004, 2020.
