53 citations as recorded by crossref.

1. Robust water level measurement method based on computer vision D. Zhang & J. Tong 10.1016/j.jhydrol.2023.129456
2. A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar H. Shui et al. 10.3390/s22031236
3. Drone-Based Water Level Detection in Flood Disasters H. Rizk et al. 10.3390/ijerph19010237
4. ATeX: A Benchmark for Image Classification of Water in Different Waterbodies Using Deep Learning Approaches S. Erfani & E. Goharian 10.1061/(ASCE)WR.1943-5452.0001615
5. The need for training and benchmark datasets for convolutional neural networks in flood applications A. Khouakhi et al. 10.2166/nh.2022.093
6. Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network R. Dhaya & R. Kanthavel 10.32604/iasc.2022.021538
7. A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions Q. Zhou et al. 10.5194/hess-27-1791-2023
8. Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management M. Oberascher et al. 10.1016/j.scs.2021.103442
9. 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. 10.5194/hess-27-2035-2023
10. Scalable flood inundation mapping using deep convolutional networks and traffic signage B. Alizadeh & A. Behzadan 10.1007/s43762-023-00090-1
11. An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks L. Windheuser et al. 10.1029/2022EA002385
12. floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time J. Hofmann & H. Schüttrumpf 10.3390/w13162255
13. A two-stage image process for water level recognition via dual-attention CornerNet and CTransformer R. Qiu et al. 10.1007/s00371-022-02501-6
14. A review of recent advances in urban flood research C. Agonafir et al. 10.1016/j.wasec.2023.100141
15. The potential of proxy water level measurements for calibrating urban pluvial flood models M. Moy de Vitry & J. Leitão 10.1016/j.watres.2020.115669
16. 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
17. A deep learning model for predicting river flood depth and extent H. Hosseiny 10.1016/j.envsoft.2021.105186
18. Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. 10.5194/hess-26-4345-2022
19. Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi 10.3390/w15183308
20. 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
21. Hydraulic modelling of inland urban flooding: Recent advances E. Mignot & B. Dewals 10.1016/j.jhydrol.2022.127763
22. The role of deep learning in urban water management: A critical review G. Fu et al. 10.1016/j.watres.2022.118973
23. Deep Sensing of Urban Waterlogging S. Lo et al. 10.1109/ACCESS.2021.3111623
24. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
25. 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
26. Machine Learning and Urban Drainage Systems: State-of-the-Art Review S. Kwon & J. Kim 10.3390/w13243545
27. Field performance of the GaugeCam image-based water level measurement system F. Birgand et al. 10.1371/journal.pwat.0000032
28. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
29. Real-time social media sentiment analysis for rapid impact assessment of floods L. Bryan-Smith et al. 10.1016/j.cageo.2023.105405
30. Detection of Urban Flood Inundation from Traffic Images Using Deep Learning Methods P. Zhong et al. 10.1007/s11269-023-03669-9
31. Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. 10.1029/2021WR029666
32. 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
33. Eye of Horus: a vision-based framework for real-time water level measurement S. Erfani et al. 10.5194/hess-27-4135-2023
34. Microscopic identification of brazilian commercial wood species via machine-learning J. Moulin et al. 10.1590/01047760202228012978
35. 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
36. 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
37. 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
38. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
39. 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
40. Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
41. Impacts of DEM type and resolution on deep learning-based flood inundation mapping M. Fereshtehpour et al. 10.1007/s12145-024-01239-0
42. WaRENet: A Novel Urban Waterlogging Risk Evaluation Network X. Yu et al. 10.1145/3651163
43. 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
44. Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations A. Moreno-Rodenas et al. 10.1016/j.watres.2021.117482
45. Research on sports aided teaching and training decision system oriented to deep convolutional neural network Q. Mei & M. Li 10.3233/JIFS-219033
46. 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
47. 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
48. Urban Flood Drifters (UFDs): Onset of movement D. Valero et al. 10.1016/j.scitotenv.2024.171568
49. Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
50. Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. 10.1016/j.jhydrol.2022.127726
51. Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data J. Jiang et al. 10.3390/rs12061014
52. Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo 10.3390/s21165614
53. Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks Z. Guo et al. 10.1111/jfr3.12684