125 citations as recorded by crossref.

1. 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
2. Flood Risk Mapping by Remote Sensing Data and Random Forest Technique H. Farhadi & M. Najafzadeh 10.3390/w13213115
3. Capturing Regional Differences in Flood Vulnerability Improves Flood Loss Estimation N. Sairam et al. 10.3389/frwa.2022.817625
4. Transaction factors’ influence on the choice of payment by Polish consumers B. Świecka et al. 10.1016/j.jretconser.2020.102264
5. Assessment of Flood Losses with Household Responses: Agent-Based Simulation in an Urban Catchment Area L. Yang et al. 10.1007/s10666-018-9597-3
6. A review on application of data mining techniques to combat natural disasters S. Goswami et al. 10.1016/j.asej.2016.01.012
7. Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy) F. Carisi et al. 10.5194/nhess-18-2057-2018
8. How useful are complex flood damage models? K. Schröter et al. 10.1002/2013WR014396
9. Investigating Uncertainty in Developing Regional Building Inventories for Flood Damage Prediction J. Gutenson et al. 10.1061/(ASCE)NH.1527-6996.0000240
10. Applications of artificial intelligence for disaster management W. Sun et al. 10.1007/s11069-020-04124-3
11. Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School W. Jin et al. 10.1109/ACCESS.2020.3028852
12. Can Remote Sensing Technologies Capture the Extreme Precipitation Event and Its Cascading Hydrological Response? A Case Study of Hurricane Harvey Using EF5 Modeling Framework M. Chen et al. 10.3390/rs12030445
13. 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
14. Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates V. Rözer et al. 10.1029/2018EF001074
15. Evaluating Flood Exposure for Properties in Urban Areas Using a Multivariate Modelling Technique G. Torgersen et al. 10.3390/w9050318
16. Integrating Ecosystem Services Into Water Resource Management: An Indicator-Based Approach K. Shaad et al. 10.1007/s00267-021-01559-7
17. Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
18. Assessment of Business Interruption of Flood-Affected Companies Using Random Forests Z. Sultana et al. 10.3390/w10081049
19. Decision-tree analysis of factors influencing rainfall-related building structure and content damage M. Spekkers et al. 10.5194/nhess-14-2531-2014
20. Uncertainty compliant design flood estimation A. Botto et al. 10.1002/2013WR014981
21. Can urban pluvial flooding be predicted by open spatial data and weather data? S. Gaitan et al. 10.1016/j.envsoft.2016.08.007
22. Calibration and validation of FLFA<sub>rs</sub> -- a new flood loss function for Australian residential structures R. Hasanzadeh Nafari et al. 10.5194/nhess-16-15-2016
23. Predictive applications of Australian flood loss models after a temporal and spatial transfer R. Hasanzadeh Nafari & T. Ngo 10.1080/19475705.2018.1445666
24. 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
25. The object-specific flood damage database HOWAS 21 P. Kellermann et al. 10.5194/nhess-20-2503-2020
26. 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
27. Environmental Inequalities in Flood Exposure: A Matter of Scale C. Poussard et al. 10.3389/frwa.2021.633046
28. Applying big data beyond small problems in climate research B. Knüsel et al. 10.1038/s41558-019-0404-1
29. Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar W. Zin et al. 10.20965/jdr.2020.p0300
30. A generic physical vulnerability model for floods: review and concept for data-scarce regions M. Malgwi et al. 10.5194/nhess-20-2067-2020
31. Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO H. Kreibich et al. 10.1111/risa.12650
32. A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. 10.1029/2019WR026213
33. Dynamic vulnerability factors for impact-based flash flood prediction G. Terti et al. 10.1007/s11069-015-1910-8
34. A coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates E. Ahmadisharaf & A. Kalyanapu 10.1111/jfr3.12536
35. 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
36. A data-mining approach towards damage modelling for El Niño events in Peru F. Brill et al. 10.1080/19475705.2020.1818636
37. 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
38. Flood Loss Models and Risk Analysis for Private Households in Can Tho City, Vietnam D. Chinh et al. 10.3390/w9050313
39. 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
40. Flood Damage Analysis Using Machine Learning Techniques . Snehil & R. Goel 10.1016/j.procs.2020.06.011
41. New insights into US flood vulnerability revealed from flood insurance big data O. Wing et al. 10.1038/s41467-020-15264-2
42. Multivariate pluvial flood damage models L. Van Ootegem et al. 10.1016/j.eiar.2015.05.005
43. Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
44. 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
45. Social media data-based typhoon disaster assessment Z. Chen & S. Lim 10.1016/j.ijdrr.2021.102482
46. Flood hazard risk assessment model based on random forest Z. Wang et al. 10.1016/j.jhydrol.2015.06.008
47. Contribution of insurance data to cost assessment of coastal flood damage to residential buildings: insights gained from Johanna (2008) and Xynthia (2010) storm events C. André et al. 10.5194/nhess-13-2003-2013
48. Probabilistic El Niño effect on flood damages assessment F. Hooshyaripor 10.1002/joc.7494
49. Flood risk assessments at different spatial scales H. de Moel et al. 10.1007/s11027-015-9654-z
50. Developing drought impact functions for drought risk management S. Bachmair et al. 10.5194/nhess-17-1947-2017
51. Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019
52. Flood loss estimation using 3D city models and remote sensing data K. Schröter et al. 10.1016/j.envsoft.2018.03.032
53. A Comparison of Factors Driving Flood Losses in Households Affected by Different Flood Types G. Mohor et al. 10.1029/2019WR025943
54. Toward Probabilistic Prediction of Flash Flood Human Impacts G. Terti et al. 10.1111/risa.12921
55. Regional and Temporal Transferability of Multivariable Flood Damage Models D. Wagenaar et al. 10.1029/2017WR022233
56. 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
57. Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. 10.5194/nhess-20-2997-2020
58. Quantifying Flood Vulnerability Reduction via Private Precaution N. Sairam et al. 10.1029/2018EF000994
59. 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
60. 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
61. 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
62. A Review of Flood Loss Models as Basis for Harmonization and Benchmarking T. Gerl et al. 10.1371/journal.pone.0159791
63. The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model C. Sampson et al. 10.5194/hess-18-2305-2014
64. A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. 10.1007/s11069-020-04413-x
65. Bayesian network learning for natural hazard analyses K. Vogel et al. 10.5194/nhess-14-2605-2014
66. 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
67. Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network M. Aamir et al. 10.3390/s21082648
68. Comprehensive Understanding the Disaster-Causing Mechanism, Governance Dilemma and Targeted Countermeasures of Urban Pluvial Flooding in China F. Kong et al. 10.3390/w13131762
69. Comparison of two modelling strategies for 2D large-scale flood simulations S. Dazzi et al. 10.1016/j.envsoft.2021.105225
70. Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models K. Vogel et al. 10.1029/2018WR022858
71. Quantifying the Role of Vulnerability in Hurricane Damage via a Machine Learning Case Study L. Szczyrba et al. 10.1061/(ASCE)NH.1527-6996.0000460
72. Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach H. van Verseveld et al. 10.1016/j.coastaleng.2015.05.006
73. Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures R. Hasanzadeh Nafari et al. 10.5194/nhess-17-1047-2017
74. Multi-Variate Analyses of Flood Loss in Can Tho City, Mekong Delta D. Chinh et al. 10.3390/w8010006
75. Probabilistic Flood Loss Models for Companies L. Schoppa et al. 10.1029/2020WR027649
76. Multi-variable flood damage modelling with limited data using supervised learning approaches D. Wagenaar et al. 10.5194/nhess-17-1683-2017
77. Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression C. Wang et al. 10.1007/s00477-016-1242-6
78. The socioeconomic impact of climate-related hazards: flash flood impact assessment in Kuala Lumpur, Malaysia T. Bhuiyan et al. 10.1007/s11069-021-04887-3
79. Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes N. Sairam et al. 10.1029/2019WR025068
80. Mapping flood susceptibility in mountainous areas on a national scale in China G. Zhao et al. 10.1016/j.scitotenv.2017.10.037
81. Is anthropogenic land subsidence a possible driver of riverine flood-hazard dynamics? A case study in Ravenna, Italy F. Carisi et al. 10.1080/02626667.2017.1390315
82. Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events C. Chow et al. 10.1016/j.jenvman.2019.05.084
83. Assessing coastal landscape vulnerability using geospatial techniques along Vizianagaram–Srikakulam coast of Andhra Pradesh, India M. Rani et al. 10.1007/s11069-018-3414-9
84. Multi-model ensembles for assessment of flood losses and associated uncertainty R. Figueiredo et al. 10.5194/nhess-18-1297-2018
85. 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
86. Surface water floods in Switzerland: what insurance claim records tell us about the damage in space and time D. Bernet et al. 10.5194/nhess-17-1659-2017
87. Tracing the value of data for flood loss modelling K. Schröter et al. 10.1051/e3sconf/20160705005
88. Data mining and linked open data – New perspectives for data analysis in environmental research A. Lausch et al. 10.1016/j.ecolmodel.2014.09.018
89. Adaptability and transferability of flood loss functions in residential areas H. Cammerer et al. 10.5194/nhess-13-3063-2013
90. Review Article: A comparison of flood and earthquake vulnerability assessment indicators M. de Ruiter et al. 10.5194/nhess-17-1231-2017
91. Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification T. Sieg et al. 10.1371/journal.pone.0212932
92. Prediction of Heavy Rain Damage Using Deep Learning K. Lee et al. 10.3390/w12071942
93. Predictive multi-hazard hurricane data-based fragility model for residential homes C. Massarra et al. 10.1016/j.coastaleng.2019.04.008
94. An Empirical, Functional approach to Depth Damages W. Lehman et al. 10.1051/e3sconf/20160705002
95. Validation of flood risk models: Current practice and possible improvements D. Molinari et al. 10.1016/j.ijdrr.2018.10.022
96. Extending coupled hydrological-hydraulic model chains with a surrogate model for the estimation of flood losses A. Zischg et al. 10.1016/j.envsoft.2018.08.009
97. A data-driven analysis, and its limitations, of the spatial flood archive of Flanders, Belgium to assess the impact of soil sealing on flood volume and extent K. Gabriels et al. 10.1371/journal.pone.0239583
98. Driving Factors and Risk Assessment of Rainstorm Waterlogging in Urban Agglomeration Areas: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area, China F. Liu et al. 10.3390/w13060770
99. Review Article: Economic evaluation of flood damage to agriculture – review and analysis of existing methods P. Brémond et al. 10.5194/nhess-13-2493-2013
100. Application of Multiple Geographical Units Convolutional Neural Network based on neighborhood effects in urban waterlogging risk assessment in the city of Guangzhou, China Y. Shu et al. 10.1016/j.pce.2021.103054
101. Improving predictions and management of hydrological extremes through climate services B. van den Hurk et al. 10.1016/j.cliser.2016.01.001
102. Significance of chloride penetration controlling parameters in concrete: Ensemble methods W. Taffese & E. Sistonen 10.1016/j.conbuildmat.2017.02.014
103. Tree-based flood damage modeling of companies: Damage processes and model performance T. Sieg et al. 10.1002/2017WR020784
104. An Assessment of the Effectiveness of Tree-Based Models for Multi-Variate Flood Damage Assessment in Australia R. Hasanzadeh Nafari et al. 10.3390/w8070282
105. Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter) S. Shan et al. 10.1016/j.ssci.2019.02.029
106. Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe D. Paprotny et al. 10.1016/j.scitotenv.2020.140011
107. Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. 10.1111/risa.13575
108. Construction and Application of Reservoir Flood Control Operation Rules Using the Decision Tree Algorithm Y. Diao et al. 10.3390/w13243654
109. Preface: Flood-risk analysis and integrated management P. Bubeck et al. 10.5194/nhess-16-1005-2016
110. Understanding the Costs of Inaction–An Assessment of Pluvial Flood Damages in Two European Cities . Nicklin et al. 10.3390/w11040801
111. Modelling flood damages under climate change conditions – a case study for Germany F. Hattermann et al. 10.5194/nhess-14-3151-2014
112. Seamless Estimation of Hydrometeorological Risk Across Spatial Scales T. Sieg et al. 10.1029/2018EF001122
113. Developing a multivariable lookup table function for estimating flood damages of rice crop in Vietnam using a secondary research approach N. Nguyen et al. 10.1016/j.ijdrr.2021.102208
114. Estimating exposure of residential assets to natural hazards in Europe using open data D. Paprotny et al. 10.5194/nhess-20-323-2020
115. Spatial Dependence Modeling of Flood Risk Using Max-Stable Processes: The Example of Austria H. Albrecher et al. 10.3390/w12061805
116. Predicting Flood Property Insurance Claims over CONUS, Fusing Big Earth Observation Data Q. Yang et al. 10.1175/BAMS-D-21-0082.1
117. A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities G. Salvadori et al. 10.1002/2015WR017225
118. Estimation of river flood damages in Jakarta, Indonesia P. Wijayanti et al. 10.1007/s11069-016-2730-1
119. Study on Influencing Factors of Flood Damage to Single Houses using Field Survey Data C. Choi et al. 10.9798/KOSHAM.2018.18.7.535
120. Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data T. Gerl et al. 10.3390/w6082367
121. Flood loss models for residential buildings, based on the 2013 Colorado floods D. Deniz et al. 10.1007/s11069-016-2615-3
122. Survey of computational intelligence as basis to big flood management: challenges, research directions and future work F. Fotovatikhah et al. 10.1080/19942060.2018.1448896
123. Exploring the potential of multivariate depth-damage and rainfall-damage models L. Van Ootegem et al. 10.1111/jfr3.12284
124. Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS H. Mojaddadi et al. 10.1080/19475705.2017.1294113
125. 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