68 citations as recorded by crossref.

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6. Application of graph neural networks to forecast urban flood events: the case study of the 2013 flood of the Bow River, Calgary, Canada P. Costa Rocha et al. 10.1080/15715124.2024.2329243
7. Cross-modal distillation for flood extent mapping S. Garg et al. 10.1017/eds.2023.34
8. Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions J. Zwart et al. 10.1111/1752-1688.13093
9. Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data J. Li et al. 10.1016/j.heliyon.2024.e33669
10. Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling M. Bolick et al. 10.3390/w15142581
11. The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al. 10.3390/su151310543
12. Streamflow forecast model using ANN D. Sonowal et al. 10.1080/23249676.2024.2310098
13. Flood Simulation in the Complex River Basin Affected by Hydraulic Structures Using a Coupled Hydrological and Hydrodynamic Model K. Zhang et al. 10.3390/w16172383
14. Forecasting of compound ocean-fluvial floods using machine learning S. Moradian et al. 10.1016/j.jenvman.2024.121295
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16. Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al. 10.1016/j.jhydrol.2023.130350
17. Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
18. Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
19. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
20. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
21. Maximum energy entropy: A novel signal preprocessing approach for data-driven monthly streamflow forecasting A. Dariane & M. M. Behbahani 10.1016/j.ecoinf.2023.102452
22. Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling Y. Bhattarai et al. 10.1016/j.teadva.2024.200116
23. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
24. Artificial intelligence can provide accurate forecasts of extreme floods at global scale 10.1038/d41586-024-00835-w
25. Prevention/mitigation of natural disasters in urban areas J. Chai & H. Wu 10.1007/s44268-023-00002-6
26. A PANN-Based Grid Downscaling Technology and Its Application in Landslide and Flood Modeling B. Zhang et al. 10.3390/rs15205075
27. Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al. 10.1016/j.jhydrol.2023.130380
28. Real-time flood forecasting in Amo Chhu using machine learning model and internet of things K. Al Abdouli et al. 10.1080/23311916.2024.2370900
29. Pushing the frontiers in climate modelling and analysis with machine learning V. Eyring et al. 10.1038/s41558-024-02095-y
30. Synthetic Forecast Ensembles for Evaluating Forecast Informed Reservoir Operations Z. Brodeur et al. 10.1029/2023WR034898
31. Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting C. Liu et al. 10.3390/s22218532
32. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
33. Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al. 10.1016/j.scitotenv.2023.165884
34. A novel multi-step ahead forecasting model for flood based on time residual LSTM Y. Zou et al. 10.1016/j.jhydrol.2023.129521
35. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
36. High flow prediction model integrating physically and deep learning based approaches with quasi real-time watershed data assimilation M. Jeong et al. 10.1016/j.jhydrol.2024.131304
37. Forecasting Floods Using Deep Learning Models: A Longitudinal Case Study of Chenab River, Pakistan K. Aatif et al. 10.1109/ACCESS.2024.3445586
38. Machine learning applications for weather and climate need greater focus on extremes P. Watson 10.1088/1748-9326/ac9d4e
39. New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting P. Costa Rocha et al. 10.3390/environments10120217
40. HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya F. Meyer et al. 10.3390/rs16173244
41. Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution H. Tedla et al. 10.1016/j.jhydrol.2024.131076
42. Creating Sustainable Flood Maps Using Machine Learning and Free Remote Sensing Data in Unmapped Areas H. Venegas-Quiñones et al. 10.3390/su16208918
43. Integration of machine learning and hydrodynamic modeling to solve the extrapolation problem in flood depth estimation H. Nguyen et al. 10.2166/wcc.2023.573
44. Predicting Eastern Mediterranean Flash Floods Using Support Vector Machines with Precipitable Water Vapor, Pressure, and Lightning Data S. Asaly et al. 10.3390/rs15112916
45. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia 10.1016/j.jhydrol.2023.130421
46. Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods V. Oliveira Santos et al. 10.3390/rs16111870
47. Review of big-data and AI application in typhoon-related disaster risk early warning in Typhoon Committee region J. Liu et al. 10.1016/j.tcrr.2023.12.004
48. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
49. A flood Impact-Based forecasting system by fuzzy inference techniques G. Wee et al. 10.1016/j.jhydrol.2023.130117
50. Estimation of river discharge using Monte Carlo simulations and a 1D hydraulic model based on the artificial multi-segmented rating curves at the confluence of two rivers H. Kang et al. 10.1088/2515-7620/ad277c
51. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al. 10.1061/JHYEFF.HEENG-6246
52. A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
53. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
54. Improving short-term streamflow forecasting by flow mode clustering S. Liu et al. 10.1007/s00477-022-02367-z
55. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
56. Machine Learning-Based Flood Forecasting System for Window Cliffs State Natural Area, Tennessee G. Darkwah et al. 10.3390/geohazards5010004
57. Impacts of DEM type and resolution on deep learning-based flood inundation mapping M. Fereshtehpour et al. 10.1007/s12145-024-01239-0
58. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
59. Flood Simulations Using a Sensor Network and Support Vector Machine Model J. Langhammer 10.3390/w15112004
60. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
61. Leveraging mesh modularization to lower the computational cost of localized updates to regional 2D hydrodynamic model outputs M. Garcia et al. 10.1080/19942060.2023.2225584
62. Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al. 10.1016/j.jhydrol.2024.130771
63. Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations J. Zwart et al. 10.3389/frwa.2023.1184992
64. Reservoir-based flood forecasting and warning: deep learning versus machine learning S. Yi & J. Yi 10.1007/s13201-024-02298-w
65. Exploring the potential of deep learning for streamflow forecasting: A comparative study with hydrological models for seasonal and perennial rivers A. Izadi et al. 10.1016/j.eswa.2024.124139
66. Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting C. Liu et al. 10.1016/j.jenvman.2024.121260
67. Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia G. Wedajo et al. 10.3390/rs16122163
68. Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh A. Rajab et al. 10.3390/w15223970