38 citations as recorded by crossref.

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3. 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
4. A climate-adaptive transfer learning framework for improving soil moisture estimation in the Qinghai-Tibet Plateau J. Yang et al. 10.1016/j.jhydrol.2024.130717
5. Eye of Horus: a vision-based framework for real-time water level measurement S. Erfani et al. 10.5194/hess-27-4135-2023
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7. Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning☆ P. Malekzadeh et al. 10.1016/j.neucom.2023.01.076
8. A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology Z. Wu et al. 10.3390/w15183233
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11. Practical application of time-lapse camera imagery to develop water-level data for three hydrologic monitoring sites in Wisconsin during water year 2020 K. Johnson et al. 10.1016/j.hydroa.2024.100199
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13. Assessing Deep Learning Techniques for Remote Gauging and Water Quality Monitoring Using Webcam Images R. Xu & B. Wang 10.3390/hydrology12040065
14. Noncontact Automatic Water-Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learning F. Mendonça et al. 10.3390/electronics13061145
15. A review of video‐based rainfall measurement methods K. Yan et al. 10.1002/wat2.1678
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17. Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection F. Fernandes Junior et al. 10.3390/s21227506
18. Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates M. Tedesco & J. Radzikowski 10.3390/geohazards4040025
19. Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions R. Xiong et al. 10.1021/acs.est.2c02232
20. Temporary flood marks proposal: What we learned after losing the baroque artifact from Cracow, Poland R. Szczepanek et al. 10.1016/j.ijdrr.2022.102942
21. Evaluation of deep learning computer vision for water level measurements in rivers W. Liu & W. Huang 10.1016/j.heliyon.2024.e25989
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23. Stage and discharge prediction from documentary time-lapse imagery K. Chapman et al. 10.1371/journal.pwat.0000106
24. Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi 10.3390/w15183308
25. 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
26. Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee 10.1007/s11269-023-03714-7
27. A deep learning workflow enhanced with optical flow fields for flood risk estimation C. Ranieri et al. 10.1007/s10489-024-05466-2
28. 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. 10.1109/ACCESS.2024.3385425
29. Refraction-based waterlogging depth measurement using solely traffic cameras for transparent flood monitoring J. Qin & P. Shen 10.1016/j.jhydrol.2025.132917
30. Bringing vision to climate: A hierarchical model for water depth monitoring in headwater streams X. Zhao et al. 10.1016/j.inffus.2024.102448
31. Cascade method for water level measurement based on computer vision D. Zhang & J. Qiu 10.1016/j.envsoft.2024.106285
32. Deep learning for automated trash screen blockage detection using cameras: Actionable information for flood risk management R. Vandaele et al. 10.2166/hydro.2024.013
33. Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
34. Identification of pedestrian submerged parts in urban flooding based on images and deep learning J. Jiang et al. 10.1016/j.envsoft.2024.106252
35. Development of Deep Intelligence for Automatic River Detection (RivDet) S. Lee et al. 10.3390/rs17020346
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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. 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