233 citations as recorded by crossref.

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16. Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances V. Kumar et al.
17. Mapping groundwater-related flooding in urban coastal regions M. Marshall et al.
18. Multisource Flood Risk Assessment for Shenyang Townships Using Dynamic Integration of Analytic Network Process and Autoencoder Weights Q. Yan et al.
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20. An autoencoder driven deep learning geospatial approach to flood vulnerability analysis in the upper and middle basin of river Damodar R. Thappitla et al.
21. A Multisensor-Based Measurement Procedure for Seismic Damage Identification in Buildings G. Salerno et al.
22. Optimized Deep Learning Model for Flood Detection Using Satellite Images A. Stateczny et al.
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.
24. Climate adaptation-aware flood prediction for coastal cities using Deep Learning B. Hassan et al.
25. Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review A. Pugliese Viloria et al.
26. Monitoring the 2024 Abrupt Flood Event in East Dongting Lake via Deep Learning and Multisource Remote Sensing Data Y. Xiao et al.
27. Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks R. Ropero et al.
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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
32. SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation W. Song et al.
33. ConvLSTM-Based Surrogate Modeling for Flood Inflow Prediction at Namgang Dam J. Sung et al.
34. An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations M. Fathi et al.
35. Flood Modeling Using HEC-RAS Based on Extreme Rainfall Data G. San-Deo
36. 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.
37. Pakistan's 2022 floods: Spatial distribution, causes and future trends from Sentinel-1 SAR observations F. Chen et al.
38. Attention Driven YOLOv5 Network for Enhanced Landslide Detection Using Satellite Imagery of Complex Terrain N. Chandra et al.
39. Cloud Modelling of Property-Level Flood Exposure in Megacities C. Iliadis et al.
40. S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images M. Wieland et al.
41. 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.
42. A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms M. Panahi et al.
43. Estimating elements susceptible to urban flooding using multisource data and machine learning W. Asfaw et al.
44. Dominant flood types in mountains catchments: Identification and change analysis for the landscape planning A. Sikorska-Senoner et al.
45. 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.
46. Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al.
47. Flood-LLM: An AI-Driven Framework for Property-Level Flood Risk Assessment Using Multi-Source Urban Data J. Jiang et al.
48. 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.
49. 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.
50. 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.
51. Continuous flood monitoring using on-demand SAR data acquired with different geometries: Methodology and test on COSMO-SkyMed images L. Pulvirenti et al.
52. Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data Y. Hahn et al.
53. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al.
54. Bayesian inference of numerical modeling-based morphodynamics: Application to a dam-break over a mobile bed experiment C. Goeury & F. Souillé
55. Evaluating the effectiveness of NDWI in SWE-GNN-based flood modelling for the Pemali River Basin, Brebes Regency, Indonesia M. Pangestu et al.
56. A data-driven approach to real-time reconstruction of turbulent flow fields above a coarse-grain bed using sparse observational boundary data Y. Yang et al.
57. Accelerating urban flood prediction using a dual-stream Transformer‑CNN model with spatiotemporal feature fusion and uncertainty quantification W. Gao et al.
58. Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan U. Rasool et al.
59. A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity F. Zheng et al.
60. A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling F. Karim et al.
61. Effects of climate change on vaccine storage and cold chain logistics: a qualitative study in Ogun State, Nigeria D. Ayowole et al.
62. Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework M. Fathi et al.
63. Review on Risk Assessment of Power System under Rainstorm-Flood and Disaster Control Improvement Strategies W. Wu et al.
64. Riverine Flood Mapping Methods and Criteria: A Meta-Analysis Review and Synthesized Guidelines R. El Moumen et al.
65. Segmentation and Visualization of Flooded Areas Through Sentinel-1 Images and U-Net F. Pech-May et al.
66. Runoff Prediction Method Based on Pangu-Weather W. Yang et al.
67. Predictive understanding of socioeconomic flood impact in data-scarce regions based on channel properties and storm characteristics: application in High Mountain Asia (HMA) M. Khanam et al.
68. Pluvial flood susceptibility mapping for data-scarce urban areas using graph attention network and basic flood conditioning factors Z. Wang et al.
69. Machine Learning-Based Flood Susceptibility Mapping Using Geoenvironmental Factors in Central Morocco M. Mohajane et al.
70. Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region E. Dike et al.
71. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al.
72. Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data S. Surampudi & V. Kumar
73. 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.
74. Assessing the coastal hazard of Medicane Ianos through ensemble modelling C. Ferrarin et al.
75. From flood forecasts to rapid assessments of risk and impacts: Establishing operational modelling frameworks for coastal flood management at the German Baltic Sea coast A. Vafeidis et al.
76. Metaheuristic-driven enhancement of categorical boosting algorithm for flood-prone areas mapping S. Razavi-Termeh et al.
77. Generative artificial intelligence and urban transport resilience: Rethinking sustainable mobility J. Wu et al.
78. 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.
79. Transferability of machine/deep learning-based prediction of fluvial flood extent to distinct river sections in Slovakia based on benchmark flood maps and high-resolution spatial data M. Vojtek et al.
80. Enhancing climate resilience in urban areas through satellite data: A case study of flood mapping during a rainfall event in Tuscany, Italy B. Carlini et al.
81. Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia G. Wedajo et al.
82. Artificial intelligence for flood risk management: A comprehensive state-of-the-art review and future directions Z. Liu et al.
83. Data driven real-time prediction of urban floods with spatial and temporal distribution S. Berkhahn & I. Neuweiler
84. DeepFlood for Inundated Vegetation High-Resolution Dataset for Accurate Flood Mapping and Segmentation M. Fawakherji et al.
85. AI-Enhanced Remote Sensing of Land Transformations for Climate-Related Financial Risk Assessment in Housing Markets: A Review C. Zhang & X. Li
86. An attention-based super-resolution model for high-accuracy urban pluvial flood forecasting in coastal megacities H. Shen et al.
87. Improving interpretability of deep active learning for flood inundation mapping through class ambiguity indices using multi-spectral satellite imagery H. Lee & W. Li
88. Flood susceptibility mapping in El Niño Phenomenom integrating multitemporal radar analysis, GIS and machine learning techniques, Piura river basin, Peru E. Badillo-Rivera et al.
89. Assessment of flood risk using Hierarchical Analysis Process method and Remote Sensing systems through arid catchment in southeastern Tunisia S. Jemai et al.
90. A dual-model framework combining nonlinear autoregressive with exogenous inputs (NARX) and LSTM networks for enhanced daily runoff prediction and error correction W. Shi et al.
91. Coupled sink and flow accumulation analyses with single flow direction and multiple flow direction algorithms N. Bowsher et al.
92. Flood bend flow prediction in intermittent river reach using a 2D hydraulic model and stacking-ensemble-based LSTM technique W. Guo et al.
93. Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model W. Gao et al.
94. Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco S. Hitouri et al.
95. Assessing Inundation Semantic Segmentation Models Trained on High- versus Low-Resolution Labels using FloodPlanet, a Manually Labeled Multi-Sourced High-Resolution Flood Dataset Z. Zhang et al.
96. Advancing flood disaster management: leveraging deep learning and remote sensing technologies M. Roohi et al.
97. AI for climate impacts: applications in flood risk A. Jones et al.
98. A novel multi-model ensemble framework for fluvial flood inundation mapping N. Mangukiya et al.
99. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al.
100. Deep vision-based framework for coastal flood prediction under sea level rise and shoreline protection A. Karapetyan et al.
101. Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning R. Palmitessa et al.
102. Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al.
103. FADS-Fusion: A Post-Flood Assessment Using Dempster–Shafer Fusion for Segmentation and Uncertainty Mapping D. Sobien & C. Sobien
104. Quick large-scale spatiotemporal flood inundation computation using integrated Encoder-Decoder LSTM with time distributed spatial output models G. Wei et al.
105. Integrated Data-Driven Multi-Criteria Analysis and Machine Learning Approaches for Assessment of Flood Susceptibility Mapping M. Rashid et al.
106. Advancing early warning systems: AI applications, challenges and emerging insights N. Otero et al.
107. Data-driven approaches to built environment flood resilience: A scientometric and critical review P. Rathnasiri et al.
108. Flood modeling and fluvial dynamics: A scoping review on the role of sediment transport H. Hamidifar et al.
109. Neuroprotective Effects of Bacterial Melanin in a Rotenone-Induced Parkinson’s Disease Rat Model: Electrophysiological Evidence from Cortical Stimulation of Substantia Nigra Neurons J. Sarkissian et al.
110. Modelling urban stormwater drainage overflows for assessing flood hazards: application to the urban area of Dakar (Senegal) L. Diémé et al.
111. An explainable deep neural network with frequency-aware channel and spatial refinement for flood prediction in sustainable cities S. Dar et al.
112. Automated floodwater depth estimation using large multimodal model for rapid flood mapping T. Akinboyewa et al.
113. Flood susceptibility mapping in data-scarce arid environments: guided by geology-driven knowledge and multi-event cloud-based validation H. Chihi et al.
114. Challenges and opportunities in flood mapping and modeling of next-generation geospatial intelligence: a review M. Shirmohammadi et al.
115. Unsupervised Color-Based Flood Segmentation in UAV Imagery G. Simantiris & C. Panagiotakis
116. A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events B. Burrichter et al.
117. AI-Driven Smart Dam Management System for Enhanced Safety and Flood Prevention B. Nirmal & S. Shekapure
118. A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach M. Wahba et al.
119. Flash flood risk mapping using analytic hierarchy process and machine learning: case of Souk-Ahras City, Northeastern Algeria M. Elhadi et al.
120. Hybrid methods in flood inundation modeling: a systematic review M. Perera et al.
121. Taşkın Tahmininde Farklı Havzaların Kullanılması; Artvin Taşkınlarının İncelenmesi Örneği U. Yükseler & Ö. Dursun
122. Beyond clouds: Seamless flood mapping using Harmonized Landsat and Sentinel-2 time series imagery and water occurrence data Z. Li et al.
123. Subgrid informed neural networks for high-resolution flood mapping H. Herath et al.
124. A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping M. El baida et al.
125. Pathways to enhanced microscale coastal flood risk assessment amid changing climate A. Kaur et al.
126. Enhancing flood prediction in Southern West Bengal, India using ensemble machine learning models optimized with symbiotic organisms search algorithm G. Hinge et al.
127. Hybrid machine learning models for urban flood susceptibility assessment in semi-arid Beni Mellal, Morocco: a spatial and comparative analysis M. El Haou et al.
128. Drowning overconfidence with uncertainty: mitigating deep learning overconfidence in flood depth super-resolution through maximum entropy regularization M. El baida et al.
129. Characterizing Storm-Induced Coastal Flooding Using SAR Imagery and Deep Learning D. Edwing et al.
130. FLOOD PREDICTION USING RANDOM FOREST TRAINED ON HYDRODYNAMIC SIMULATIONS UNDER HYPOTHETICAL DESIGN RAINFALLS K. LIU et al.
131. Rapid Prediction of Compound Flood Based on Hydrological-Hydrodynamic Model and Convolution Neural Network K. Xu et al.
132. Remote Sensing and Artificial Intelligence in Flood Prediction: Progress, Challenges, and Prospects S. ShravanKumar et al.
133. Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing J. Kim et al.
134. Modelling and Mapping Rapid-Onset Coastal Flooding: A Systematic Literature Review A. Re et al.
135. Riverine flood hazard map prediction by neural networks Z. Yin & A. Leon
136. Comparative Analysis of Storm Surge Reconstructions in the Western North Pacific: Implications for Coastal Flood Risk Assessment M. Fan et al.
137. Generalized Methodology for Two-Dimensional Flood Depth Prediction Using ML-Based Models M. Soliman et al.
138. A new framework for quantifying alongshore variability of swash motion using fully convolutional networks R. Salatin et al.
139. Optimizing Flood Susceptibility Detection Using Ensemble Learning Methods Z. Dai et al.
140. STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery N. Notarangelo et al.
141. Students' Lexical Difficulties in Classical Mechanics and Modern Physics Vocabulary: A Survey Analysis A. Falakh et al.
142. Flood mapping using Sentinel-1 imagery with topographical and hydrological contextualization: Case study from Ribe, Denmark M. Hansen et al.
143. An integrated framework for satellite-based flood mapping and socioeconomic risk analysis: A case of Thailand N. Prasertsoong & N. Puttanapong
144. A modular coupled physics-informed neural network framework for urban flood prediction B. Lee & Y. Chang
145. Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios X. Liang et al.
146. Scientific machine learning in hydrology: a unified perspective A. Adombi
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149. Enhancing cross-regional transferability of super-resolution-based flood surrogate models for data-scarce catchments W. Song & M. Guan
150. Rapid spatio-temporal flood modelling via hydraulics-based graph neural networks R. Bentivoglio et al.
151. Urban flood hazard assessment using FLA-optimized boost algorithms in Ankara, Türkiye E. Gul
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153. Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks C. do Lago et al.
154. SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks K. Halder et al.
155. A Novel Hybrid Deep-Learning Approach for Flood-Susceptibility Mapping A. Riche et al.
156. Development of a Deep Learning-Based Flooding Region Segmentation Model for Recognizing Urban Flooding Situations J. Yoo et al.
157. Assessing the spatiotemporal dynamics of seasonal and perennial surface water resources across Lesotho’s agroecological zones K. Singh et al.
158. 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.
159. Real-time flood maps forecasting for dam-break scenarios with a transformer-based deep learning model M. Pianforini et al.
160. Investigation of seismic damage to existing buildings by using remotely observed images R. Nascimbene
161. AlleyFloodNet: A Ground-Level Image Dataset for Rapid Flood Detection in Economically and Flood-Vulnerable Areas O. Lee & H. Joo
162. Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica C. Avalon-Cullen et al.
163. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al.
164. A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring M. Gross et al.
165. An intelligent VMD-WTC-GRU hybrid framework with uncertainty quantification for forecasting extreme flood events in semi-arid regions R. Ramezani et al.
166. How February 2022 redefines extreme floods in Australia J. Lerat & J. Vaze
167. Aerial Image Classification in Post Flood Scenarios Using Robust Deep Learning and Explainable Artificial Intelligence A. Manaf et al.
168. Efficient river hydrodynamics modelling in realistic river systems using a Fourier neural operator-based network M. Pang
169. Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge J. Wang et al.
170. Flood Subsidence Susceptibility Mapping using Elastic-net Classifier: New Approach A. Al-Areeq et al.
171. Flood susceptibility mapping for sustainable water management in the Upper Awash River Basin, Ethiopia: integrating machine learning and geospatial techniques M. Leta
172. Spatial Assessment of Urban Flood Resilience Using a GESIS-ML Framework: A Case Study of Chongqing, China Y. Li et al.
173. Exploring the compound nature of coastal flooding by tropical cyclones: A machine learning framework M. Di Bacco et al.
174. Rapid flood inundation mapping by integrating deep learning-based image super-resolution with coarse-grid hydrodynamic modeling W. Song et al.
175. FFM: Flood Forecasting Model Using Federated Learning M. Farooq et al.
176. A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model Y. Liao et al.
177. Fast high-fidelity flood inundation map generation by super-resolution techniques Z. Yin et al.
178. STURM-FloodDepth: A deep learning pipeline for mapping urban flood depth using street-level and oblique aerial imagery N. Notarangelo et al.
179. Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model Y. Liao et al.
180. 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.
181. Flood susceptibility mapping of urban flood risk: comparing autoencoder multilayer perceptron and logistic regression models in Ubon Ratchathani, Thailand N. Tsumita et al.
182. Adaptive Urban Stormwater Strategies by AI-Based Pumping Machinery Management and Image Recognition in Taiwan S. Hsieh et al.
183. 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.
184. Rapid Simulation of Floods by Considering the Spatial and Temporal Characteristics of Inundation R. Wang et al.
185. Flood Susceptibility Mapping of the Kosi Megafan Using Ensemble Machine Learning and SAR Data K. Khan et al.
186. Improving pluvial flood mapping resolution of large coarse models with deep learning C. Ambrogi Ferreira Do Lago et al.
187. Toward minute-level flood stereoscopic intelligence: Real-time multimodal remote sensing under an on-orbit AI paradigm J. Yao et al.
188. Assessing Flood Risk Potential using Advanced Statistical and Machine Learning Models in the Lower Gangetic Floodplain Region, India M. Kundu et al.
189. Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM) B. Baziak et al.
190. Supervised and unsupervised machine learning approaches using Sentinel data for flood mapping and damage assessment in Mozambique M. Nhangumbe et al.
191. A Similarity-Based Fuzzy Framework for Flood Damage Assessment Under Data-Scarce Conditions T. Vranić et al.
192. Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation? G. Simantiris et al.
193. Bayesian-optimized BiLSTM-U-Net framework for urban flood prediction with spatio-temporal feature integration X. Yao et al.
194. Beyond infrastructure: Re-conceptualizing flood resilient governance through systems thinking, Indigenous knowledge, and proactive planning K. Nahiduzzaman et al.
195. Advancing Flood Detection and Mapping: A Review of Earth Observation Services, 3D Data Integration, and AI-Based Techniques T. Destefanis et al.
196. Assessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models N. Fraehr et al.
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198. 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
199. Combining Machine Learning Models and Satellite Data of an Extreme Flood Event for Flood Susceptibility Mapping N. Tepetidis et al.
200. Sentinel-1 SAR Images and Deep Learning for Water Body Mapping F. Pech-May et al.
201. Drainage Network Generation for Urban Pluvial Flooding (UPF) Using Generative Adversarial Networks (GANs) and GIS Data M. Ahmad et al.
202. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks D. Yang et al.
203. A priori physical information to aid generalization capabilities of neural networks for hydraulic modeling G. Guglielmo et al.
204. Urban Pluvial Flood Modeling Integrating Two-Dimensional CNN and SHAP-Driven Interpretability W. Chen et al.
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206. Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network L. Besseling et al.
207. Smart Hydrology: The Role of Artificial Intelligence in Sustainable Soil and Water Resource Management P. Kharat et al.
208. Quantifying human impacts on surface water dynamics via a convolutional LSTM approach D. Lu et al.
209. The future of coastal monitoring through satellite remote sensing S. Vitousek et al.
210. GeoFlood Enhancement for Robust Flood Inundation Mapping in Flat Terrain Zones M. Wahba et al.
211. 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.
212. Assessing urban flood susceptibility in Seoul, South Korea using machine learning models: effects of urban infrastructure and sampling variability Y. Lee et al.
213. Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems K. Glynis et al.
214. Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation C. Chang et al.
215. Understanding the spatial patterns of major geo-hydrological disasters in Italy using the CatBoost algorithm S. Segoni et al.
216. Enhancing flood forecasting with deep learning: A scalable alternative to traditional hydrodynamic models W. Duangkhwan et al.
217. 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
218. Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations N. Fraehr et al.
219. Flash Flood Susceptibility Modelling Using Soft Computing-Based Approaches: From Bibliometric to Meta-Data Analysis and Future Research Directions G. Hinge et al.
220. A novel approach in comparing the performance of bivariate statistical methods, boosting, and stacking models in flood susceptibility assessment L. Hanh et al.
221. Contribution and behavioral assessment of physical and anthropogenic factors for soil erosion using integrated deep learning and game theory I. Ahmed et al.
222. Performance and uncertainty analysis in deep learning frameworks for streamflow forecasting via Monte Carlo dropout technique X. Le et al.
223. Knowledge-Driven Flood Intelligent Monitoring (KDFIM) Method: Analyzing the Kakhovka Dam Destruction Incident Z. Jiao et al.
224. Solving flood problems with deep learning technology: Research status, strategies, and future directions H. Li et al.
225. Real-Time Urban Flood Depth Mapping: Convolutional Neural Networks for Pluvial and Fluvial Flood Emulation M. El baida et al.
226. Flood and Non-Flood Image Classification using Deep Ensemble Learning E. Yasi et al.
227. SSegRef2Surf—Near Real-Time Photogrammetric Flood Monitoring and Refinement of Classified Water Surfaces M. Kögel et al.
228. Flood hazard mapping using M5 tree algorithms and logistic regression: a case study in East Black Sea Region U. Yukseler et al.
229. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al.
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