141 citations as recorded by crossref.

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21. Modelling and Mapping Rapid-Onset Coastal Flooding: A Systematic Literature Review A. Re et al. 10.3390/w17040599
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24. Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review A. Pugliese Viloria et al. 10.3390/rs16183374
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27. Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany O. Seleem et al. 10.5194/nhess-23-809-2023
28. STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery N. Notarangelo et al. 10.1080/20964471.2025.2458714
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30. A hybrid statistical–dynamical framework for compound coastal flooding analysis Z. Wang et al. 10.1088/1748-9326/ad96ce
31. A novel approach to flood risk zonation: integrating deep learning models with APG in the Aji Chay catchment A. Abdollahzadeh Bina & S. Fard Moradinia 10.2166/aqua.2024.023
32. SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation W. Song et al. 10.1016/j.jhydrol.2025.133280
33. An integrated framework for satellite-based flood mapping and socioeconomic risk analysis: A case of Thailand N. Prasertsoong & N. Puttanapong 10.1016/j.pdisas.2024.100393
34. Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios X. Liang et al. 10.1016/j.apenergy.2023.121642
35. Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability Z. Wang et al. 10.1016/j.jhydrol.2024.131903
36. Pakistan's 2022 floods: Spatial distribution, causes and future trends from Sentinel-1 SAR observations F. Chen et al. 10.1016/j.rse.2024.114055
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41. S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images M. Wieland et al. 10.1109/JSTARS.2023.3333969
42. Geo-environmental GIS modeling to predict flood hazard in heavy rainfall eastern Himalaya region: a precautionary measure towards disaster risk reduction P. Rawat et al. 10.1007/s10661-025-13652-z
43. A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms M. Panahi et al. 10.1029/2023EF003749
44. Estimating elements susceptible to urban flooding using multisource data and machine learning W. Asfaw et al. 10.1016/j.ijdrr.2024.105169
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46. Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks C. do Lago et al. 10.1016/j.jhydrol.2023.129276
47. A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area L. Melgar-García et al. 10.1080/17538947.2023.2252401
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49. Flood susceptibility analysis to sustainable development using MCDA and support vector machine models by GIS in the selected area of the Teesta River floodplain, Bangladesh S. Akhter et al. 10.1016/j.hydres.2024.10.004
50. Integrated approaches for flash flood susceptibility mapping: spatial modeling and comparative analysis of statistical and machine learning models. A case study of the Rheraya watershed, Morocco A. Elghouat et al. 10.2166/wcc.2024.726
51. Flood Susceptibility Mapping Using Image‐Based 2D‐CNN Deep Learning: Overview and Case Study Application Using Multiparametric Spatial Data in Data‐Scarce Urban Environments Y. Ouma et al. 10.1155/2023/5672401
52. A Novel Hybrid Deep-Learning Approach for Flood-Susceptibility Mapping A. Riche et al. 10.3390/rs16193673
53. Development of a Deep Learning-Based Flooding Region Segmentation Model for Recognizing Urban Flooding Situations J. Yoo et al. 10.3390/su162411041
54. 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
55. Real-time flood maps forecasting for dam-break scenarios with a transformer-based deep learning model M. Pianforini et al. 10.1016/j.jhydrol.2024.131169
56. Investigation of seismic damage to existing buildings by using remotely observed images R. Nascimbene 10.1016/j.engfailanal.2024.108282
57. Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica C. Avalon-Cullen et al. 10.3390/geosciences13030064
58. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. 10.3390/rs16040656
59. A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring M. Gross et al. 10.1016/j.envsoft.2024.106247
60. Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data Y. Hahn et al. 10.3390/w16233368
61. Aerial Image Classification in Post Flood Scenarios Using Robust Deep Learning and Explainable Artificial Intelligence A. Manaf et al. 10.1109/ACCESS.2025.3543078
62. Efficient river hydrodynamics modelling in realistic river systems using a Fourier neural operator-based network M. Pang 10.1016/j.jhydrol.2024.131345
63. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
64. Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge J. Wang et al. 10.3390/rs17091540
65. Flood Subsidence Susceptibility Mapping using Elastic-net Classifier: New Approach A. Al-Areeq et al. 10.1007/s11269-023-03591-0
66. Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan U. Rasool et al. 10.1016/j.uclim.2023.101573
67. A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity F. Zheng et al. 10.1016/j.jhydrol.2024.131706
68. A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling F. Karim et al. 10.3390/w15030566
69. Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework M. Fathi et al. 10.1016/j.envsoft.2024.106307
70. Review on Risk Assessment of Power System under Rainstorm-Flood and Disaster Control Improvement Strategies W. Wu et al. 10.1088/1742-6596/2774/1/012077
71. Exploring the compound nature of coastal flooding by tropical cyclones: A machine learning framework M. Di Bacco et al. 10.1016/j.jhydrol.2024.132262
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79. Flood susceptibility mapping using qualitative and statistical methods in a semi-arid basin: case of the Manouba–Sijoumi watershed, Northeastern Tunisia N. Khadraoui et al. 10.1007/s11600-022-00966-6
80. Pluvial flood susceptibility mapping for data-scarce urban areas using graph attention network and basic flood conditioning factors Z. Wang et al. 10.1080/10106049.2023.2275692
81. Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region E. Dike et al. 10.3390/jmse11081528
82. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
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84. Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data S. Surampudi & V. Kumar 10.1016/j.rsase.2024.101361
85. Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China C. Wen et al. 10.3390/rs17050904
86. Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM) B. Baziak et al. 10.3390/hydrology11020017
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91. Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia G. Wedajo et al. 10.3390/rs16122163
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137. A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure N. Silva-Cancino et al. 10.3390/w14152416
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139. Integrated assessment of flood susceptibility and exposure rate in the lower Niger Basin, Onitsha, Southeastern Nigeria A. Chinedu et al. 10.3389/feart.2024.1394256
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