73 citations as recorded by crossref.

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22. The potential of open-access data for flood estimations: uncovering inundation hotspots in Ho Chi Minh City, Vietnam, through a normalized flood severity index L. Scheiber et al. 10.5194/nhess-23-2313-2023
23. Identification of pedestrian submerged parts in urban flooding based on images and deep learning J. Jiang et al. 10.1016/j.envsoft.2024.106252
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27. Exploring Generative AI for YOLO-Based Object Detection to Enhance Flood Disaster Response in Malaysia J. Teoh et al. 10.1109/ACCESS.2024.3498893
28. Designing pluvial flood-safe urban landscapes using differentiable surrogate flood models Z. Guo & J. Leitão 10.2166/bgs.2025.042
29. Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi 10.3390/w15183308
30. Urban hydrological model (UHM) developed for an urban flash flood simulation and analysis of the flood intensity sensitivity to urbanization H. Hu et al. 10.1080/19475705.2024.2302561
31. Hydraulic modelling of inland urban flooding: Recent advances E. Mignot & B. Dewals 10.1016/j.jhydrol.2022.127763
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33. Deep Sensing of Urban Waterlogging S. Lo et al. 10.1109/ACCESS.2021.3111623
34. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
35. From risk control to resilience: developments and trends of urban roads designed as surface flood passages to cope with extreme storms Z. Shao et al. 10.1007/s11783-024-1782-9
36. Urban Flood Inundation Area Detection using YOLOv8 Model F. Xue et al. 10.1007/s11269-025-04211-9
37. Machine Learning and Urban Drainage Systems: State-of-the-Art Review S. Kwon & J. Kim 10.3390/w13243545
38. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. 10.3390/w16091286
39. Bringing vision to climate: A hierarchical model for water depth monitoring in headwater streams X. Zhao et al. 10.1016/j.inffus.2024.102448
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41. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
42. Real-time social media sentiment analysis for rapid impact assessment of floods L. Bryan-Smith et al. 10.1016/j.cageo.2023.105405
43. Detection of Urban Flood Inundation from Traffic Images Using Deep Learning Methods P. Zhong et al. 10.1007/s11269-023-03669-9
44. Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. 10.1029/2021WR029666
45. Current and Emerging Deep Learning Methods for Flood Simulation B. Li & Y. Gao 10.62051/ijcsit.v2n2.12
46. 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
47. Eye of Horus: a vision-based framework for real-time water level measurement S. Erfani et al. 10.5194/hess-27-4135-2023
48. Microscopic identification of brazilian commercial wood species via machine-learning J. Moulin et al. 10.1590/01047760202228012978
49. Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects D. Chitwatkulsiri & H. Miyamoto 10.3390/w15010178
50. How to use general AI for task-specific applications: A case study of monitoring water level trends with river cameras Z. Wang et al. 10.1016/j.envsoft.2025.106550
51. Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms Z. Qiao et al. 10.3390/rs13224662
52. Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization W. Lee & E. Lee 10.3390/w14010099
53. Deep learning and LiDAR integration for surveillance camera-based river water level monitoring in flood applications N. Muhadi et al. 10.1007/s11069-024-06503-6
54. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
55. Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas W. Mobley et al. 10.5194/nhess-21-807-2021
56. Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
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. WaRENet: A Novel Urban Waterlogging Risk Evaluation Network X. Yu et al. 10.1145/3651163
59. Assessing Deep Learning Techniques for Remote Gauging and Water Quality Monitoring Using Webcam Images R. Xu & B. Wang 10.3390/hydrology12040065
60. Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. 10.5194/hess-25-4435-2021
61. Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations A. Moreno-Rodenas et al. 10.1016/j.watres.2021.117482
62. Research on sports aided teaching and training decision system oriented to deep convolutional neural network Q. Mei & M. Li 10.3233/JIFS-219033
63. Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network Y. Wang et al. 10.1016/j.envsoft.2023.105939
64. Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis A. Safaei-Moghadam et al. 10.5194/nhess-23-1-2023
65. Urban Flood Drifters (UFDs): Onset of movement D. Valero et al. 10.1016/j.scitotenv.2024.171568
66. Do we need to label large datasets for river water segmentation? Benchmark and stage estimation with minimum to non-labeled image time series P. Zamboni et al. 10.1080/01431161.2025.2457131
67. Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
68. Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. 10.1016/j.jhydrol.2022.127726
69. Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data J. Jiang et al. 10.3390/rs12061014
70. Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo 10.3390/s21165614
71. Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks Z. Guo et al. 10.1111/jfr3.12684
72. Deep learning and LiDAR integration for surveillance camera-based river water level monitoring in flood applications N. Muhadi et al. 10.1007/s11069-024-06503-6
73. Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network M. Moy de Vitry et al. 10.5194/hess-23-4621-2019