47 citations as recorded by crossref.

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5. 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
6. Cross-modal distillation for flood extent mapping S. Garg et al. 10.1017/eds.2023.34
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8. Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling M. Bolick et al. 10.3390/w15142581
9. 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
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11. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
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14. 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
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18. Prevention/mitigation of natural disasters in urban areas J. Chai & H. Wu 10.1007/s44268-023-00002-6
19. A PANN-Based Grid Downscaling Technology and Its Application in Landslide and Flood Modeling B. Zhang et al. 10.3390/rs15205075
20. 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
21. Synthetic Forecast Ensembles for Evaluating Forecast Informed Reservoir Operations Z. Brodeur et al. 10.1029/2023WR034898
22. Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting C. Liu et al. 10.3390/s22218532
23. 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
24. A novel multi-step ahead forecasting model for flood based on time residual LSTM Y. Zou et al. 10.1016/j.jhydrol.2023.129521
25. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
26. Machine learning applications for weather and climate need greater focus on extremes P. Watson 10.1088/1748-9326/ac9d4e
27. New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting P. Costa Rocha et al. 10.3390/environments10120217
28. 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
29. 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
30. Predicting Eastern Mediterranean Flash Floods Using Support Vector Machines with Precipitable Water Vapor, Pressure, and Lightning Data S. Asaly et al. 10.3390/rs15112916
31. 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
32. 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
33. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
34. A flood Impact-Based forecasting system by fuzzy inference techniques G. Wee et al. 10.1016/j.jhydrol.2023.130117
35. 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
36. A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
37. Improving short-term streamflow forecasting by flow mode clustering S. Liu et al. 10.1007/s00477-022-02367-z
38. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
39. Machine Learning-Based Flood Forecasting System for Window Cliffs State Natural Area, Tennessee G. Darkwah et al. 10.3390/geohazards5010004
40. Impacts of DEM type and resolution on deep learning-based flood inundation mapping M. Fereshtehpour et al. 10.1007/s12145-024-01239-0
41. 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
42. Flood Simulations Using a Sensor Network and Support Vector Machine Model J. Langhammer 10.3390/w15112004
43. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
44. 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
45. 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
46. Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations J. Zwart et al. 10.3389/frwa.2023.1184992
47. Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh A. Rajab et al. 10.3390/w15223970