67 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
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26. Exploring Generative AI for YOLO-Based Object Detection to Enhance Flood Disaster Response in Malaysia J. Teoh et al. 10.1109/ACCESS.2024.3498893
27. Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi 10.3390/w15183308
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32. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
33. 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
34. Machine Learning and Urban Drainage Systems: State-of-the-Art Review S. Kwon & J. Kim 10.3390/w13243545
35. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. 10.3390/w16091286
36. 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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38. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
39. Real-time social media sentiment analysis for rapid impact assessment of floods L. Bryan-Smith et al. 10.1016/j.cageo.2023.105405
40. Detection of Urban Flood Inundation from Traffic Images Using Deep Learning Methods P. Zhong et al. 10.1007/s11269-023-03669-9
41. Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. 10.1029/2021WR029666
42. 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
43. Eye of Horus: a vision-based framework for real-time water level measurement S. Erfani et al. 10.5194/hess-27-4135-2023
44. Microscopic identification of brazilian commercial wood species via machine-learning J. Moulin et al. 10.1590/01047760202228012978
45. 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
46. 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
47. 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
48. 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
49. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
50. 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
51. Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
52. Impacts of DEM type and resolution on deep learning-based flood inundation mapping M. Fereshtehpour et al. 10.1007/s12145-024-01239-0
53. WaRENet: A Novel Urban Waterlogging Risk Evaluation Network X. Yu et al. 10.1145/3651163
54. 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
55. Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations A. Moreno-Rodenas et al. 10.1016/j.watres.2021.117482
56. Research on sports aided teaching and training decision system oriented to deep convolutional neural network Q. Mei & M. Li 10.3233/JIFS-219033
57. 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
58. 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
59. Urban Flood Drifters (UFDs): Onset of movement D. Valero et al. 10.1016/j.scitotenv.2024.171568
60. 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
61. Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
62. Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. 10.1016/j.jhydrol.2022.127726
63. Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data J. Jiang et al. 10.3390/rs12061014
64. Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo 10.3390/s21165614
65. Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks Z. Guo et al. 10.1111/jfr3.12684
66. 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
67. 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