86 citations as recorded by crossref.

1. A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery A. Moghimi et al. https://doi.org/10.1109/ACCESS.2024.3385425
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3. Robust water level measurement method based on computer vision D. Zhang & J. Tong https://doi.org/10.1016/j.jhydrol.2023.129456
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5. Drone-Based Water Level Detection in Flood Disasters H. Rizk et al. https://doi.org/10.3390/ijerph19010237
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8. Cascade method for water level measurement based on computer vision D. Zhang & J. Qiu https://doi.org/10.1016/j.envsoft.2024.106285
9. Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network R. Dhaya & R. Kanthavel https://doi.org/10.32604/iasc.2022.021538
10. Detection of Urban Road Waterlogging Using Deep Learning Methods Y. Gao et al. https://doi.org/10.1007/s11269-026-04679-z
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13. Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management M. Oberascher et al. https://doi.org/10.1016/j.scs.2021.103442
14. An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system W. Zhu et al. https://doi.org/10.5194/hess-27-2035-2023
15. Deep learning for automated trash screen blockage detection using cameras: Actionable information for flood risk management R. Vandaele et al. https://doi.org/10.2166/hydro.2024.013
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20. floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time J. Hofmann & H. Schüttrumpf https://doi.org/10.3390/w13162255
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23. A Cyber-Physical System for Real-Time Flood Monitoring: Integration of Semantic Segmentation and Edge Computing in Taiwan Y. Fang et al. https://doi.org/10.3390/w18111286
24. Deep Neural Network-Based Flood Monitoring System Fusing RGB and LWIR Cameras for Embedded IoT Edge Devices Y. Lee et al. https://doi.org/10.3390/rs16132358
25. The potential of proxy water level measurements for calibrating urban pluvial flood models M. Moy de Vitry & J. Leitão https://doi.org/10.1016/j.watres.2020.115669
26. 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. https://doi.org/10.5194/nhess-23-2313-2023
27. Identification of pedestrian submerged parts in urban flooding based on images and deep learning J. Jiang et al. https://doi.org/10.1016/j.envsoft.2024.106252
28. Depth estimation in urban flooding using surveillance cameras and high-resolution LiDAR data M. Zamanizadeh et al. https://doi.org/10.1016/j.envsoft.2025.106572
29. A deep learning model for predicting river flood depth and extent H. Hosseiny https://doi.org/10.1016/j.envsoft.2021.105186
30. Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. https://doi.org/10.5194/hess-26-4345-2022
31. Exploring Generative AI for YOLO-Based Object Detection to Enhance Flood Disaster Response in Malaysia J. Teoh et al. https://doi.org/10.1109/ACCESS.2024.3498893
32. Designing pluvial flood-safe urban landscapes using differentiable surrogate flood models Z. Guo & J. Leitão https://doi.org/10.2166/bgs.2025.042
33. Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi https://doi.org/10.3390/w15183308
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38. Deep Sensing of Urban Waterlogging S. Lo et al. https://doi.org/10.1109/ACCESS.2021.3111623
39. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. https://doi.org/10.3390/hydrology10080158
40. 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. https://doi.org/10.1007/s11783-024-1782-9
41. Urban Flood Inundation Area Detection using YOLOv8 Model F. Xue et al. https://doi.org/10.1007/s11269-025-04211-9
42. Intelligent water level measurement based on visual foundation models Z. Wu et al. https://doi.org/10.1016/j.measurement.2025.118502
43. Machine Learning and Urban Drainage Systems: State-of-the-Art Review S. Kwon & J. Kim https://doi.org/10.3390/w13243545
44. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. https://doi.org/10.3390/w16091286
45. AI image-based method for a robust automatic real-time water level monitoring: a long-term application case X. Blanch et al. https://doi.org/10.5194/hess-30-797-2026
46. Bringing vision to climate: A hierarchical model for water depth monitoring in headwater streams X. Zhao et al. https://doi.org/10.1016/j.inffus.2024.102448
47. Field performance of the GaugeCam image-based water level measurement system F. Birgand et al. https://doi.org/10.1371/journal.pwat.0000032
48. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. https://doi.org/10.3390/w15091760
49. Real-time social media sentiment analysis for rapid impact assessment of floods L. Bryan-Smith et al. https://doi.org/10.1016/j.cageo.2023.105405
50. Detection of Urban Flood Inundation from Traffic Images Using Deep Learning Methods P. Zhong et al. https://doi.org/10.1007/s11269-023-03669-9
51. Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. https://doi.org/10.1029/2021WR029666
52. Current and Emerging Deep Learning Methods for Flood Simulation B. Li & Y. Gao https://doi.org/10.62051/ijcsit.v2n2.12
53. Community-scale urban flood monitoring through fusion of time-lapse imagery, terrestrial lidar, and remote sensing data J. Dale et al. https://doi.org/10.5194/hess-30-2183-2026
54. 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. https://doi.org/10.3390/su151310543
55. FloodForecaster: A domain-adaptive geometry-informed neural operator framework for rapid flood forecasting M. Taghizadeh et al. https://doi.org/10.1016/j.jhydrol.2025.134512
56. Towards real-time water level and discharge measurements using imagery, machine learning, and edge computing R. Issa et al. https://doi.org/10.2166/hydro.2026.147
57. Eye of Horus: a vision-based framework for real-time water level measurement S. Erfani et al. https://doi.org/10.5194/hess-27-4135-2023
58. Microscopic identification of brazilian commercial wood species via machine-learning J. Moulin et al. https://doi.org/10.1590/01047760202228012978
59. Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects D. Chitwatkulsiri & H. Miyamoto https://doi.org/10.3390/w15010178
60. How to use general AI for task-specific applications: A case study of monitoring water level trends with river cameras Z. Wang et al. https://doi.org/10.1016/j.envsoft.2025.106550
61. 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. https://doi.org/10.3390/rs13224662
62. 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 https://doi.org/10.3390/w14010099
63. Deep learning and LiDAR integration for surveillance camera-based river water level monitoring in flood applications N. Muhadi et al. https://doi.org/10.1007/s11069-024-06503-6
64. Robust and Fast Sensing of Urban Flood Depth with Social Media Images Using Pre-Trained Large Models and Simple Edge Training L. Lin et al. https://doi.org/10.3390/hydrology12110307
65. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. https://doi.org/10.2166/wst.2020.369
66. 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. https://doi.org/10.5194/nhess-21-807-2021
67. Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. https://doi.org/10.3390/app11209691
68. An intelligent sensing framework for urban pluvial flood quantification and drainage infrastructure resilience based on transformer architectures Y. Zhou et al. https://doi.org/10.1016/j.jwpe.2026.110454
69. Impacts of DEM type and resolution on deep learning-based flood inundation mapping M. Fereshtehpour et al. https://doi.org/10.1007/s12145-024-01239-0
70. WaRENet: A Novel Urban Waterlogging Risk Evaluation Network X. Yu et al. https://doi.org/10.1145/3651163
71. Assessing Deep Learning Techniques for Remote Gauging and Water Quality Monitoring Using Webcam Images R. Xu & B. Wang https://doi.org/10.3390/hydrology12040065
72. Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. https://doi.org/10.5194/hess-25-4435-2021
73. Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations A. Moreno-Rodenas et al. https://doi.org/10.1016/j.watres.2021.117482
74. Research on sports aided teaching and training decision system oriented to deep convolutional neural network Q. Mei & M. Li https://doi.org/10.3233/JIFS-219033
75. Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network Y. Wang et al. https://doi.org/10.1016/j.envsoft.2023.105939
76. Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis A. Safaei-Moghadam et al. https://doi.org/10.5194/nhess-23-1-2023
77. Urban Flood Drifters (UFDs): Onset of movement D. Valero et al. https://doi.org/10.1016/j.scitotenv.2024.171568
78. HydrocamCollect: A robust data acquisition and cloud data transfer workflow for camera-based hydrological monitoring S. Neupane et al. https://doi.org/10.1016/j.envsoft.2025.106770
79. ITS-Vision: Autonomous Vehicles as Mobile Surveillance Nodes in Intelligent Transportation Systems—A Conceptual Framework and Proof-of-Concept Prototype M. Medvei et al. https://doi.org/10.3390/fi18070349
80. Quantifying flood model accuracy under varying surface complexities W. Addison-Atkinson et al. https://doi.org/10.1016/j.jhydrol.2023.129511
81. 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. https://doi.org/10.1080/01431161.2025.2457131
82. A Conceptual Approach to Predicting Seismic Events and Flood Risks Using Convolutional Neural Networks M. Rehan et al. https://doi.org/10.33093/jiwe.2025.4.2.9
83. Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. https://doi.org/10.1017/eds.2023.6
84. Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. https://doi.org/10.1016/j.jhydrol.2022.127726
85. Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data J. Jiang et al. https://doi.org/10.3390/rs12061014
86. Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo https://doi.org/10.3390/s21165614