60 citations as recorded by crossref.

1. Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model X. Fu et al. 10.3389/feart.2022.1102802
2. Empirical flash flood vulnerability functions for residential buildings C. Arrighi et al. 10.1007/s42452-020-2696-1
3. Augmenting a socio-hydrological flood risk model for companies with process-oriented loss estimation L. Schoppa et al. 10.1080/02626667.2022.2095207
4. A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach M. Wahba et al. 10.3390/w15234138
5. Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019
6. A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. 10.1007/s11069-020-04413-x
7. Characterization of damages in buildings after floods in Vega Baja County (Spain) in 2019. The case study of Almoradí municipality R. Moya Barbera et al. 10.1016/j.cscm.2024.e03004
8. Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas W. Mobley et al. 10.5194/nhess-21-807-2021
9. A comparison of building value models for flood risk analysis V. Röthlisberger et al. 10.5194/nhess-18-2431-2018
10. Probabilistic Assessment of Pluvial Flood Risk Across 20 European Cities: A Demonstrator of the Copernicus Disaster Risk Reduction Service for Pluvial Flood Risk in Urban Areas A. Essenfelder et al. 10.1142/S2382624X22400070
11. Evaluation of residential building damage for the July 2021 flood in Westport, New Zealand R. Paulik et al. 10.1186/s40562-024-00323-z
12. Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event M. Di Bacco et al. 10.1016/j.envsoft.2022.105604
13. A generic physical vulnerability model for floods: review and concept for data-scarce regions M. Malgwi et al. 10.5194/nhess-20-2067-2020
14. Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. 10.5194/nhess-20-2997-2020
15. Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models C. Liu et al. 10.1080/01431161.2023.2277163
16. Assessment of microscale economic flood losses in urban and agricultural areas: case study of the Santa Bárbara River, Ecuador J. Pinos et al. 10.1007/s11069-020-04084-8
17. Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach M. Museru et al. 10.1016/j.scitotenv.2023.167872
18. Leveraging machine learning for predicting flash flood damage in the Southeast US A. Alipour et al. 10.1088/1748-9326/ab6edd
19. Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning X. Jiang et al. 10.1016/j.isprsjprs.2021.05.019
20. Estimating exposure of residential assets to natural hazards in Europe using open data D. Paprotny et al. 10.5194/nhess-20-323-2020
21. The role of socio-economic and property variables in the establishment of flood depth-damage curve for the data-scarce area in Malaysia S. Sulong & N. Romali 10.1080/1573062X.2022.2099292
22. A new framework for flood damage assessment considering the within-event time evolution of hazard, exposure, and vulnerability T. Lazzarin et al. 10.1016/j.jhydrol.2022.128687
23. Machine learning models to predict myocardial infarctions from past climatic and environmental conditions L. Marien et al. 10.5194/nhess-22-3015-2022
24. Probabilistic Flood Loss Models for Companies L. Schoppa et al. 10.1029/2020WR027649
25. Flood depth-damage and fragility functions derived with structured expert judgment G. Pita et al. 10.1016/j.jhydrol.2021.126982
26. Bayesian Data-Driven approach enhances synthetic flood loss models N. Sairam et al. 10.1016/j.envsoft.2020.104798
27. Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
28. Evaluating the spatial application of multivariable flood damage models R. Paulik et al. 10.1111/jfr3.12934
29. Regional and Temporal Transferability of Multivariable Flood Damage Models D. Wagenaar et al. 10.1029/2017WR022233
30. Flood Vulnerability Models and Household Flood Damage Mitigation Measures: An Econometric Analysis of Survey Data T. Endendijk et al. 10.1029/2022WR034192
31. An empirical flood fatality model for Italy using random forest algorithm M. Yazdani et al. 10.1016/j.ijdrr.2023.104110
32. A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. 10.1029/2019WR026213
33. Expert-based versus data-driven flood damage models: A comparative evaluation for data-scarce regions M. Malgwi et al. 10.1016/j.ijdrr.2021.102148
34. Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
35. Construction of flood loss function for cities lacking disaster data based on three-dimensional (object-function-array) data processing H. Lv et al. 10.1016/j.scitotenv.2021.145649
36. A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment J. Carreau & V. Guinot 10.1016/j.advwatres.2020.103821
37. The potential of open-access data for flood estimations: uncovering inundation hotspots in Ho Chi Minh City, Vietnam, through a normalized flood severity index L. Scheiber et al. 10.5194/nhess-23-2313-2023
38. Evaluating adaptation measures for reducing flood risk: A case study in the city of Colombo, Sri Lanka D. Wagenaar et al. 10.1016/j.ijdrr.2019.101162
39. Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts R. Paulik et al. 10.3390/jmse11101960
40. The construction of flood loss ratio function in cities lacking loss data based on dynamic proportional substitution and hierarchical Bayesian model H. Lv et al. 10.1016/j.jhydrol.2020.125797
41. Systemic Financial Risk Arising From Residential Flood Losses H. Thomson et al. 10.1029/2022EF003206
42. A machine learning-based prediction and analysis of flood affected households: A case study of floods in Bangladesh K. Ganguly et al. 10.1016/j.ijdrr.2018.12.002
43. Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings O. Aribisala et al. 10.3390/su142113817
44. A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis R. Harris et al. 10.1016/j.crm.2022.100410
45. A data-mining approach towards damage modelling for El Niño events in Peru F. Brill et al. 10.1080/19475705.2020.1818636
46. Impact of an Integrated Approach in Disaster Management O. Njoku et al. 10.4018/IJOCI.2020040102
47. Improving flood impact estimations T. Sieg & A. Thieken 10.1088/1748-9326/ac6d6c
48. Flood loss estimation using 3D city models and remote sensing data K. Schröter et al. 10.1016/j.envsoft.2018.03.032
49. Flood Damage Analysis Using Machine Learning Techniques . Snehil & R. Goel 10.1016/j.procs.2020.06.011
50. Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes N. Sairam et al. 10.1029/2019WR025068
51. Validation of flood risk models: Current practice and possible improvements D. Molinari et al. 10.1016/j.ijdrr.2018.10.022
52. Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties H. Lv et al. 10.1029/2021WR031552
53. Probabilistic framework for quantifying infrastructure systems’ resilience against floods A. Daneshifar & H. Kashani 10.1080/23789689.2024.2328977
54. Preface: Damage of natural hazards: assessment and mitigation H. Kreibich et al. 10.5194/nhess-19-551-2019
55. Flood Risk Modeling under Uncertainties: The Case Study of Croatia T. Kekez et al. 10.3390/w14101585
56. Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates V. Rözer et al. 10.1029/2018EF001074
57. A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood M. Malgwi et al. 10.1007/s11069-021-04756-z
58. Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. 10.1111/risa.13575
59. Seamless Estimation of Hydrometeorological Risk Across Spatial Scales T. Sieg et al. 10.1029/2018EF001122
60. Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019