144 citations as recorded by crossref.

1. A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events B. Burrichter et al. 10.3390/hydrology11030041
2. A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach M. Wahba et al. 10.3390/w15234138
3. Taşkın Tahmininde Farklı Havzaların Kullanılması; Artvin Taşkınlarının İncelenmesi Örneği U. Yükseler & Ö. Dursun 10.35234/fumbd.1438170
4. Beyond clouds: Seamless flood mapping using Harmonized Landsat and Sentinel-2 time series imagery and water occurrence data Z. Li et al. 10.1016/j.isprsjprs.2024.07.022
5. MatFlood: An efficient algorithm for mapping flood extent and depth A. Enriquez et al. 10.1016/j.envsoft.2023.105829
6. Subgrid informed neural networks for high-resolution flood mapping H. Herath et al. 10.1016/j.jhydrol.2025.133329
7. A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping M. El baida et al. 10.1007/s11269-024-03940-7
8. Comprehensive Flood Risk Assessment: State of the Practice N. Grigg 10.3390/hydrology10020046
9. NeuralFlood: an AI-driven flood susceptibility index J. Lin et al. 10.3389/frwa.2023.1291305
10. Enhancing flood prediction in Southern West Bengal, India using ensemble machine learning models optimized with symbiotic organisms search algorithm G. Hinge et al. 10.1007/s00477-024-02712-4
11. Real time probabilistic inundation forecasts using a LSTM neural network F. Hop et al. 10.1016/j.jhydrol.2024.131082
12. Multi-scale hydraulic graph neural networks for flood modelling R. Bentivoglio et al. 10.5194/nhess-25-335-2025
13. Drowning overconfidence with uncertainty: mitigating deep learning overconfidence in flood depth super-resolution through maximum entropy regularization M. El baida et al. 10.1007/s00477-025-02917-1
14. Characterizing Storm-Induced Coastal Flooding Using SAR Imagery and Deep Learning D. Edwing et al. 10.1109/JSTARS.2025.3530255
15. Artificial intelligence for modeling and understanding extreme weather and climate events G. Camps-Valls et al. 10.1038/s41467-025-56573-8
16. Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances V. Kumar et al. 10.3390/hydrology10070141
17. Mapping groundwater-related flooding in urban coastal regions M. Marshall et al. 10.1016/j.jhydrol.2025.132907
18. Choice of Gaussian Process kernels used in LSG models for flood inundation predictions J. Lu et al. 10.1016/j.jhydrol.2025.132949
19. Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing J. Kim et al. 10.3390/rs14246373
20. Optimized Deep Learning Model for Flood Detection Using Satellite Images A. Stateczny et al. 10.3390/rs15205037
21. Modelling and Mapping Rapid-Onset Coastal Flooding: A Systematic Literature Review A. Re et al. 10.3390/w17040599
22. Riverine flood hazard map prediction by neural networks Z. Yin & A. Leon 10.1016/j.hydres.2024.10.003
23. Flood susceptibility mapping through geoinformatics and ensemble learning methods, with an emphasis on the AdaBoost-Decision Tree algorithm, in Mazandaran, Iran M. Jahanbani et al. 10.1007/s12145-023-01213-2
24. Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review A. Pugliese Viloria et al. 10.3390/rs16183374
25. Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks R. Ropero et al. 10.1007/s10651-023-00587-2
26. A new framework for quantifying alongshore variability of swash motion using fully convolutional networks R. Salatin et al. 10.1016/j.coastaleng.2024.104542
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
29. Emerging strategies for addressing flood-damage modeling issues: A review S. Redondo-Tilano et al. 10.1016/j.ijdrr.2024.105058
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
37. An efficient 2-D flood inundation modelling based on a data-driven approach S. Chiang et al. 10.1016/j.ejrh.2024.101741
38. Rapid spatio-temporal flood modelling via hydraulics-based graph neural networks R. Bentivoglio et al. 10.5194/hess-27-4227-2023
39. Urban flood hazard assessment using FLA-optimized boost algorithms in Ankara, Türkiye E. Gul 10.1007/s13201-025-02424-2
40. Cloud Modelling of Property-Level Flood Exposure in Megacities C. Iliadis et al. 10.3390/w15193395
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
45. Dominant flood types in mountains catchments: Identification and change analysis for the landscape planning A. Sikorska-Senoner et al. 10.1016/j.jenvman.2023.119905
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
48. SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks K. Halder et al. 10.1080/19475705.2024.2409202
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. Continuous flood monitoring using on-demand SAR data acquired with different geometries: Methodology and test on COSMO-SkyMed images L. Pulvirenti et al. 10.1016/j.isprsjprs.2025.04.036
61. Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data Y. Hahn et al. 10.3390/w16233368
62. Aerial Image Classification in Post Flood Scenarios Using Robust Deep Learning and Explainable Artificial Intelligence A. Manaf et al. 10.1109/ACCESS.2025.3543078
63. Efficient river hydrodynamics modelling in realistic river systems using a Fourier neural operator-based network M. Pang 10.1016/j.jhydrol.2024.131345
64. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
65. Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge J. Wang et al. 10.3390/rs17091540
66. Flood Subsidence Susceptibility Mapping using Elastic-net Classifier: New Approach A. Al-Areeq et al. 10.1007/s11269-023-03591-0
67. Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan U. Rasool et al. 10.1016/j.uclim.2023.101573
68. 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
69. A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling F. Karim et al. 10.3390/w15030566
70. 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
71. 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
72. 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
73. Rapid flood inundation mapping by integrating deep learning-based image super-resolution with coarse-grid hydrodynamic modeling W. Song et al. 10.1080/19942060.2025.2481115
74. FFM: Flood Forecasting Model Using Federated Learning M. Farooq et al. 10.1109/ACCESS.2023.3252896
75. A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model Y. Liao et al. 10.1007/s13753-023-00481-2
76. Segmentation and Visualization of Flooded Areas Through Sentinel-1 Images and U-Net F. Pech-May et al. 10.1109/JSTARS.2024.3387452
77. Fast high-fidelity flood inundation map generation by super-resolution techniques Z. Yin et al. 10.2166/hydro.2024.228
78. Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model Y. Liao et al. 10.1016/j.jhydrol.2023.129945
79. Artificial neural network-based multi-input multi-output model for short-term storm surge prediction on the southeast coast of China Y. Qin et al. 10.1016/j.oceaneng.2024.116915
80. Runoff Prediction Method Based on Pangu-Weather W. Yang et al. 10.3390/w17091405
81. 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
82. 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
83. 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
84. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
85. Improving pluvial flood mapping resolution of large coarse models with deep learning C. Ambrogi Ferreira Do Lago et al. 10.1080/02626667.2024.2329268
86. 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
87. 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
88. Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM) B. Baziak et al. 10.3390/hydrology11020017
89. Supervised and unsupervised machine learning approaches using Sentinel data for flood mapping and damage assessment in Mozambique M. Nhangumbe et al. 10.1016/j.rsase.2023.101015
90. Assessing the coastal hazard of Medicane Ianos through ensemble modelling C. Ferrarin et al. 10.5194/nhess-23-2273-2023
91. Metaheuristic-driven enhancement of categorical boosting algorithm for flood-prone areas mapping S. Razavi-Termeh et al. 10.1016/j.jag.2025.104357
92. Improved metamodels for predicting high-dimensional outputs by accounting for the dependence structure of the latent variables: application to marine flooding J. Rohmer et al. 10.1007/s00477-023-02426-z
93. 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
94. Artificial intelligence for flood risk management: A comprehensive state-of-the-art review and future directions Z. Liu et al. 10.1016/j.ijdrr.2024.105110
95. Data driven real-time prediction of urban floods with spatial and temporal distribution S. Berkhahn & I. Neuweiler 10.1016/j.hydroa.2023.100167
96. DeepFlood for Inundated Vegetation High-Resolution Dataset for Accurate Flood Mapping and Segmentation M. Fawakherji et al. 10.1038/s41597-025-04554-3
97. Improving interpretability of deep active learning for flood inundation mapping through class ambiguity indices using multi-spectral satellite imagery H. Lee & W. Li 10.1016/j.rse.2024.114213
98. Assessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models N. Fraehr et al. 10.1016/j.watres.2024.121202
99. Generation and selection of training events for surrogate flood inundation models N. Fraehr et al. 10.1016/j.jenvman.2024.123570
100. Enhanced flood prediction using LSTM and climate parameters: multi-station analysis of snowmelt-induced flooding in the Red River of the North V. Atashi & H. Gorji 10.2166/hydro.2025.236
101. Sentinel-1 SAR Images and Deep Learning for Water Body Mapping F. Pech-May et al. 10.3390/rs15123009
102. Drainage Network Generation for Urban Pluvial Flooding (UPF) Using Generative Adversarial Networks (GANs) and GIS Data M. Ahmad et al. 10.3390/su17104380
103. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks D. Yang et al. 10.3390/app14093845
104. A priori physical information to aid generalization capabilities of neural networks for hydraulic modeling G. Guglielmo et al. 10.3389/fcpxs.2024.1508091
105. Assessment of flood risk using Hierarchical Analysis Process method and Remote Sensing systems through arid catchment in southeastern Tunisia S. Jemai et al. 10.1016/j.jaridenv.2024.105150
106. Coupled sink and flow accumulation analyses with single flow direction and multiple flow direction algorithms N. Bowsher et al. 10.2166/hydro.2023.123
107. Flood bend flow prediction in intermittent river reach using a 2D hydraulic model and stacking-ensemble-based LSTM technique W. Guo et al. 10.1007/s12145-024-01526-w
108. Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique H. Huang et al. 10.1016/j.ecoinf.2024.102904
109. Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model W. Gao et al. 10.1016/j.jhydrol.2024.132228
110. Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network L. Besseling et al. 10.3390/hydrology11090152
111. Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco S. Hitouri et al. 10.3390/rs16050858
112. The future of coastal monitoring through satellite remote sensing S. Vitousek et al. 10.1017/cft.2022.4
113. A semi-supervised multi-temporal landslide and flash flood event detection methodology for unexplored regions using massive satellite image time series A. Deijns et al. 10.1016/j.isprsjprs.2024.07.010
114. Advancing flood disaster management: leveraging deep learning and remote sensing technologies M. Roohi et al. 10.1007/s11600-024-01481-6
115. Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems K. Glynis et al. 10.1007/s11269-023-03637-3
116. Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation C. Chang et al. 10.1016/j.jhydrol.2024.131308
117. Quantitative evaluation of flood extent detection using attention U-Net case studies from Eastern South Wales Australia in March 2021 and July 2022 F. Fakhri & I. Gkanatsios 10.1038/s41598-025-92734-x
118. Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations N. Fraehr et al. 10.1029/2022WR033836
119. AI for climate impacts: applications in flood risk A. Jones et al. 10.1038/s41612-023-00388-1
120. Flash Flood Susceptibility Modelling Using Soft Computing-Based Approaches: From Bibliometric to Meta-Data Analysis and Future Research Directions G. Hinge et al. 10.3390/w16010173
121. A novel multi-model ensemble framework for fluvial flood inundation mapping N. Mangukiya et al. 10.1016/j.envsoft.2024.106163
122. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
123. Contribution and behavioral assessment of physical and anthropogenic factors for soil erosion using integrated deep learning and game theory I. Ahmed et al. 10.1016/j.jclepro.2023.137689
124. Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning R. Palmitessa et al. 10.1016/j.watres.2022.118972
125. Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al. 10.5194/hess-28-5443-2024
126. Solving flood problems with deep learning technology: Research status, strategies, and future directions H. Li et al. 10.1002/sd.3074
127. Quick large-scale spatiotemporal flood inundation computation using integrated Encoder-Decoder LSTM with time distributed spatial output models G. Wei et al. 10.1016/j.jhydrol.2024.130993
128. Real-Time Urban Flood Depth Mapping: Convolutional Neural Networks for Pluvial and Fluvial Flood Emulation M. El baida et al. 10.1007/s11269-024-03886-w
129. Flood and Non-Flood Image Classification using Deep Ensemble Learning E. Yasi et al. 10.1007/s11269-024-03906-9
130. SSegRef2Surf—Near Real-Time Photogrammetric Flood Monitoring and Refinement of Classified Water Surfaces M. Kögel et al. 10.3390/rs17081351
131. Flood hazard mapping using M5 tree algorithms and logistic regression: a case study in East Black Sea Region U. Yukseler et al. 10.1007/s12145-023-01013-8
132. Data-driven approaches to built environment flood resilience: A scientometric and critical review P. Rathnasiri et al. 10.1016/j.aei.2023.102085
133. Flood modeling and fluvial dynamics: A scoping review on the role of sediment transport H. Hamidifar et al. 10.1016/j.earscirev.2024.104775
134. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. 10.3390/w16091286
135. Modelling urban stormwater drainage overflows for assessing flood hazards: application to the urban area of Dakar (Senegal) L. Diémé et al. 10.5194/nhess-25-1095-2025
136. Automated floodwater depth estimation using large multimodal model for rapid flood mapping T. Akinboyewa et al. 10.1007/s43762-024-00123-3
137. Large-scale flood modeling and forecasting with FloodCast Q. Xu et al. 10.1016/j.watres.2024.122162
138. Unsupervised Color-Based Flood Segmentation in UAV Imagery G. Simantiris & C. Panagiotakis 10.3390/rs16122126
139. Unraveling the complexities of urban fluvial flood hydraulics through AI M. Mehedi et al. 10.1038/s41598-022-23214-9
140. 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
141. Modeling rainfall-induced 2D inundation simulation based on the ANN-derived models with precipitation and water-level measurements at roadside IoT sensors S. Wu 10.1038/s41598-023-44276-3
142. 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
143. Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
144. An Efficient U-Net Model for Improved Landslide Detection from Satellite Images N. Chandra et al. 10.1007/s41064-023-00232-4