106 citations as recorded by crossref.

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16. Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review A. Pugliese Viloria et al. 10.3390/rs16183374
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20. 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
21. 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
22. 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
23. 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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26. Cloud Modelling of Property-Level Flood Exposure in Megacities C. Iliadis et al. 10.3390/w15193395
27. 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
28. A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms M. Panahi et al. 10.1029/2023EF003749
29. 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
30. Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks C. do Lago et al. 10.1016/j.jhydrol.2023.129276
31. 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
32. SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks K. Halder et al. 10.1080/19475705.2024.2409202
33. 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
34. 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
35. 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
36. A Novel Hybrid Deep-Learning Approach for Flood-Susceptibility Mapping A. Riche et al. 10.3390/rs16193673
37. 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
38. 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
39. Investigation of seismic damage to existing buildings by using remotely observed images R. Nascimbene 10.1016/j.engfailanal.2024.108282
40. Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica C. Avalon-Cullen et al. 10.3390/geosciences13030064
41. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. 10.3390/rs16040656
42. A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring M. Gross et al. 10.1016/j.envsoft.2024.106247
43. Efficient river hydrodynamics modelling in realistic river systems using a Fourier neural operator-based network M. Pang 10.1016/j.jhydrol.2024.131345
44. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
45. Flood Subsidence Susceptibility Mapping using Elastic-net Classifier: New Approach A. Al-Areeq et al. 10.1007/s11269-023-03591-0
46. Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan U. Rasool et al. 10.1016/j.uclim.2023.101573
47. 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
48. A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling F. Karim et al. 10.3390/w15030566
49. 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
50. 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
51. FFM: Flood Forecasting Model Using Federated Learning M. Farooq et al. 10.1109/ACCESS.2023.3252896
52. 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
53. Segmentation and Visualization of Flooded Areas Through Sentinel-1 Images and U-Net F. Pech-May et al. 10.1109/JSTARS.2024.3387452
54. Fast high-fidelity flood inundation map generation by super-resolution techniques Z. Yin et al. 10.2166/hydro.2024.228
55. 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
56. 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
57. 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
58. 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
59. 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
60. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
61. Improving pluvial flood mapping resolution of large coarse models with deep learning C. Ambrogi Ferreira Do Lago et al. 10.1080/02626667.2024.2329268
62. 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
63. Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM) B. Baziak et al. 10.3390/hydrology11020017
64. 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
65. Assessing the coastal hazard of Medicane Ianos through ensemble modelling C. Ferrarin et al. 10.5194/nhess-23-2273-2023
66. 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
67. 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
68. Data driven real-time prediction of urban floods with spatial and temporal distribution S. Berkhahn & I. Neuweiler 10.1016/j.hydroa.2023.100167
69. 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
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72. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks D. Yang et al. 10.3390/app14093845
73. 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
74. Coupled sink and flow accumulation analyses with single flow direction and multiple flow direction algorithms N. Bowsher et al. 10.2166/hydro.2023.123
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77. Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network L. Besseling et al. 10.3390/hydrology11090152
78. Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco S. Hitouri et al. 10.3390/rs16050858
79. The future of coastal monitoring through satellite remote sensing S. Vitousek et al. 10.1017/cft.2022.4
80. 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
81. 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
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85. 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
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