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21. A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people S. Balbi et al. 10.5194/nhess-16-1323-2016
22. Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates V. Rözer et al. 10.1029/2018EF001074
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26. Assessment of Business Interruption of Flood-Affected Companies Using Random Forests Z. Sultana et al. 10.3390/w10081049
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28. Decision-tree analysis of factors influencing rainfall-related building structure and content damage M. Spekkers et al. 10.5194/nhess-14-2531-2014
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35. Above the Ravines: Flood Vulnerability Assessment of Earthen Architectural Heritage in Quito (Ecuador) F. Trizio et al. 10.3390/app122311932
36. Predictive applications of Australian flood loss models after a temporal and spatial transfer R. Hasanzadeh Nafari & T. Ngo 10.1080/19475705.2018.1445666
37. Flood vulnerability and risk assessment of urban traditional buildings in a heritage district of Kuala Lumpur, Malaysia D. D'Ayala et al. 10.5194/nhess-20-2221-2020
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41. Assessing the impact of climate change on fluvial flood losses in urban areas: a case study of Pamplona (Spain) E. Soriano et al. 10.1080/02626667.2023.2246452
42. Multi-variate residential flood loss estimation model for Jakarta: an approach based on a combination of statistical techniques R. Wahab & R. Tiong 10.1007/s11069-016-2716-z
43. Environmental Inequalities in Flood Exposure: A Matter of Scale C. Poussard et al. 10.3389/frwa.2021.633046
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47. Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT‐FLEMO H. Kreibich et al. 10.1111/risa.12650
48. A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. 10.1029/2019WR026213
49. Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods J. Chen et al. 10.3390/app14093694
50. Using a High-Precision Flood Risk Assessment Tool to Understand Commercial Building and Market Impacts in the United States J. Porter 10.2139/ssrn.3981118
51. Machine learning models to predict myocardial infarctions from past climatic and environmental conditions L. Marien et al. 10.5194/nhess-22-3015-2022
52. The utility of using Volunteered Geographic Information (VGI) for evaluating pluvial flood models M. Drews et al. 10.1016/j.scitotenv.2023.164962
53. Dynamic vulnerability factors for impact-based flash flood prediction G. Terti et al. 10.1007/s11069-015-1910-8
54. A coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates E. Ahmadisharaf & A. Kalyanapu 10.1111/jfr3.12536
55. A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data S. Dong et al. 10.1111/mice.12629
56. Empirical flood depth damage functions based on insurance data from multiple flood events in Japan Y. Tamaki et al. 10.3178/hrl.24-00025
57. Distributed hydrodynamic modelling for assessing flood impacts on crops: Assessing flood-resilient crop management in a coastal basin of central Italy T. Lucaora et al. 10.1016/j.agwat.2025.109352
58. Flood vulnerability assessment in the flood prone area of Khyber Pakhtunkhwa, Pakistan M. Ibrahim et al. 10.3389/fenvs.2024.1303976
59. A data-mining approach towards damage modelling for El Niño events in Peru F. Brill et al. 10.1080/19475705.2020.1818636
60. Improvements and Operational Application of a Zero-Order Quick Assessment Model for Flood Damage: A Case Study in Italy L. Manselli et al. 10.3390/w14030373
61. Flood Loss Models and Risk Analysis for Private Households in Can Tho City, Vietnam D. Chinh et al. 10.3390/w9050313
62. Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale H. Kreibich et al. 10.5194/piahs-373-179-2016
63. 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
64. Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice E. Allegri et al. 10.1016/j.scitotenv.2024.169925
65. Flood Damage Analysis Using Machine Learning Techniques . Snehil & R. Goel 10.1016/j.procs.2020.06.011
66. Improving flood impact estimations T. Sieg & A. Thieken 10.1088/1748-9326/ac6d6c
67. New insights into US flood vulnerability revealed from flood insurance big data O. Wing et al. 10.1038/s41467-020-15264-2
68. FLIDND-MCN: Fake label images detection of natural disasters with multi model convolutional neural network S. Alqhtani 10.3233/JIFS-213308
69. Multivariate pluvial flood damage models L. Van Ootegem et al. 10.1016/j.eiar.2015.05.005
70. Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
71. 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
72. Exploring drought hazard, vulnerability, and related impacts on agriculture in Brandenburg F. Brill et al. 10.5194/nhess-24-4237-2024
73. Social media data-based typhoon disaster assessment Z. Chen & S. Lim 10.1016/j.ijdrr.2021.102482
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75. Flooding: Contributing factors to residential flood damage in Canada B. Deschamps et al. 10.1016/j.ijdrr.2025.105348
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77. Integrating socio-economic variables in urban flood damage assessments: a case study of Bengaluru, India A. Pathak et al. 10.1007/s11069-024-07019-9
78. Probabilistic El Niño effect on flood damages assessment F. Hooshyaripor 10.1002/joc.7494
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81. Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019
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85. Regional and Temporal Transferability of Multivariable Flood Damage Models D. Wagenaar et al. 10.1029/2017WR022233
86. Flood damage functions based on a single physics- and data-based impact parameter that jointly accounts for water depth and velocity T. Lazzarin et al. 10.1016/j.jhydrol.2022.127485
87. Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. 10.5194/nhess-20-2997-2020
88. Quantifying Flood Vulnerability Reduction via Private Precaution N. Sairam et al. 10.1029/2018EF000994
89. Flood risk assessment of cultural heritage at large spatial scales: Framework and application to mainland Portugal R. Figueiredo et al. 10.1016/j.culher.2019.11.007
90. Handling uncertainty in bivariate quantile estimation – An application to flood hazard analysis in the Mekong Delta N. Dung et al. 10.1016/j.jhydrol.2015.05.033
91. Understanding flood risk in the context of community resilience modeling for the built environment: research needs and trends O. Nofal & J. van de Lindt 10.1080/23789689.2020.1722546
92. A Review of Flood Loss Models as Basis for Harmonization and Benchmarking T. Gerl et al. 10.1371/journal.pone.0159791
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94. A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. 10.1007/s11069-020-04413-x
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97. Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach B. Winter et al. 10.5194/nhess-20-1689-2020
98. Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network M. Aamir et al. 10.3390/s21082648
99. Comprehensive Understanding the Disaster-Causing Mechanism, Governance Dilemma and Targeted Countermeasures of Urban Pluvial Flooding in China F. Kong et al. 10.3390/w13131762
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