161 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. Assessing the impact of climate change on flood inundation and agriculture in the Himalayan Mountainous Region of Bhutan P. Syldon et al. 10.1016/j.ejrh.2024.101687
3. Flood Risk Mapping by Remote Sensing Data and Random Forest Technique H. Farhadi & M. Najafzadeh 10.3390/w13213115
4. Capturing Regional Differences in Flood Vulnerability Improves Flood Loss Estimation N. Sairam et al. 10.3389/frwa.2022.817625
5. Evaluating the spatial application of multivariable flood damage models R. Paulik et al. 10.1111/jfr3.12934
6. Transaction factors’ influence on the choice of payment by Polish consumers B. Świecka et al. 10.1016/j.jretconser.2020.102264
7. 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
8. A statistical method for pre-estimating impacts from a disaster: A case study of floods in Kaduwela, Sri Lanka C. Randil et al. 10.1016/j.ijdrr.2022.103010
9. A review on application of data mining techniques to combat natural disasters S. Goswami et al. 10.1016/j.asej.2016.01.012
10. Comprehensive assessment of flood exposure in arid regions: Integrating GIS techniques and multi-method approaches – A case study of downstream swat river, Pakistan M. Ibrahim et al. 10.1016/j.ijdrr.2024.104515
11. Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy) F. Carisi et al. 10.5194/nhess-18-2057-2018
12. How useful are complex flood damage models? K. Schröter et al. 10.1002/2013WR014396
13. Investigating Uncertainty in Developing Regional Building Inventories for Flood Damage Prediction J. Gutenson et al. 10.1061/(ASCE)NH.1527-6996.0000240
14. Applications of artificial intelligence for disaster management W. Sun et al. 10.1007/s11069-020-04124-3
15. Residential building flood damage: Insights on processes and implications for risk assessments R. Paulik et al. 10.1111/jfr3.12832
16. 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
17. 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
18. 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
19. Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates V. Rözer et al. 10.1029/2018EF001074
20. Evaluating Flood Exposure for Properties in Urban Areas Using a Multivariate Modelling Technique G. Torgersen et al. 10.3390/w9050318
21. Integrating Ecosystem Services Into Water Resource Management: An Indicator-Based Approach K. Shaad et al. 10.1007/s00267-021-01559-7
22. Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
23. Assessment of Business Interruption of Flood-Affected Companies Using Random Forests Z. Sultana et al. 10.3390/w10081049
24. Decision-tree analysis of factors influencing rainfall-related building structure and content damage M. Spekkers et al. 10.5194/nhess-14-2531-2014
25. Evaluation of residential building damage for the July 2021 flood in Westport, New Zealand R. Paulik et al. 10.1186/s40562-024-00323-z
26. Uncertainty compliant design flood estimation A. Botto et al. 10.1002/2013WR014981
27. Can urban pluvial flooding be predicted by open spatial data and weather data? S. Gaitan et al. 10.1016/j.envsoft.2016.08.007
28. The SAVEMEDCOASTS-2 webGIS: The Online Platform for Relative Sea Level Rise and Storm Surge Scenarios up to 2100 for the Mediterranean Coasts A. Falciano et al. 10.3390/jmse11112071
29. 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
30. Above the Ravines: Flood Vulnerability Assessment of Earthen Architectural Heritage in Quito (Ecuador) F. Trizio et al. 10.3390/app122311932
31. Predictive applications of Australian flood loss models after a temporal and spatial transfer R. Hasanzadeh Nafari & T. Ngo 10.1080/19475705.2018.1445666
32. 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
33. The object-specific flood damage database HOWAS 21 P. Kellermann et al. 10.5194/nhess-20-2503-2020
34. Hydrological Model Adaptability to Rainfall Inputs of Varied Quality J. Wang et al. 10.1029/2022WR032484
35. STUDY ON REHABILITATION OF HOUSES AFTER FLOOD EVENTS PART 1:DISCUSSION FROM THE VIEWPOINT OF CONSTRUCTION WORKS AND COSTS S. WATANABE et al. 10.3130/aija.88.2179
36. 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
37. 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
38. Environmental Inequalities in Flood Exposure: A Matter of Scale C. Poussard et al. 10.3389/frwa.2021.633046
39. Applying big data beyond small problems in climate research B. Knüsel et al. 10.1038/s41558-019-0404-1
40. Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar W. Zin et al. 10.20965/jdr.2020.p0300
41. A generic physical vulnerability model for floods: review and concept for data-scarce regions M. Malgwi et al. 10.5194/nhess-20-2067-2020
42. Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT‐FLEMO H. Kreibich et al. 10.1111/risa.12650
43. A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. 10.1029/2019WR026213
44. Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods J. Chen et al. 10.3390/app14093694
45. 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
46. Machine learning models to predict myocardial infarctions from past climatic and environmental conditions L. Marien et al. 10.5194/nhess-22-3015-2022
47. The utility of using Volunteered Geographic Information (VGI) for evaluating pluvial flood models M. Drews et al. 10.1016/j.scitotenv.2023.164962
48. Dynamic vulnerability factors for impact-based flash flood prediction G. Terti et al. 10.1007/s11069-015-1910-8
49. A coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates E. Ahmadisharaf & A. Kalyanapu 10.1111/jfr3.12536
50. 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
51. Flood vulnerability assessment in the flood prone area of Khyber Pakhtunkhwa, Pakistan M. Ibrahim et al. 10.3389/fenvs.2024.1303976
52. A data-mining approach towards damage modelling for El Niño events in Peru F. Brill et al. 10.1080/19475705.2020.1818636
53. 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
54. Flood Loss Models and Risk Analysis for Private Households in Can Tho City, Vietnam D. Chinh et al. 10.3390/w9050313
55. 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
56. 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
57. 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
58. Flood Damage Analysis Using Machine Learning Techniques . Snehil & R. Goel 10.1016/j.procs.2020.06.011
59. Improving flood impact estimations T. Sieg & A. Thieken 10.1088/1748-9326/ac6d6c
60. New insights into US flood vulnerability revealed from flood insurance big data O. Wing et al. 10.1038/s41467-020-15264-2
61. FLIDND-MCN: Fake label images detection of natural disasters with multi model convolutional neural network S. Alqhtani 10.3233/JIFS-213308
62. Multivariate pluvial flood damage models L. Van Ootegem et al. 10.1016/j.eiar.2015.05.005
63. Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
64. 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
65. Social media data-based typhoon disaster assessment Z. Chen & S. Lim 10.1016/j.ijdrr.2021.102482
66. Flood hazard risk assessment model based on random forest Z. Wang et al. 10.1016/j.jhydrol.2015.06.008
67. 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
68. Probabilistic El Niño effect on flood damages assessment F. Hooshyaripor 10.1002/joc.7494
69. Flood risk assessments at different spatial scales H. de Moel et al. 10.1007/s11027-015-9654-z
70. Developing drought impact functions for drought risk management S. Bachmair et al. 10.5194/nhess-17-1947-2017
71. Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019
72. Flood loss estimation using 3D city models and remote sensing data K. Schröter et al. 10.1016/j.envsoft.2018.03.032
73. A Comparison of Factors Driving Flood Losses in Households Affected by Different Flood Types G. Mohor et al. 10.1029/2019WR025943
74. Toward Probabilistic Prediction of Flash Flood Human Impacts G. Terti et al. 10.1111/risa.12921
75. Regional and Temporal Transferability of Multivariable Flood Damage Models D. Wagenaar et al. 10.1029/2017WR022233
76. 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
77. Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. 10.5194/nhess-20-2997-2020
78. Quantifying Flood Vulnerability Reduction via Private Precaution N. Sairam et al. 10.1029/2018EF000994
79. 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
80. 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
81. 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
82. A Review of Flood Loss Models as Basis for Harmonization and Benchmarking T. Gerl et al. 10.1371/journal.pone.0159791
83. 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
84. A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. 10.1007/s11069-020-04413-x
85. Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings O. Aribisala et al. 10.3390/su142113817
86. Bayesian network learning for natural hazard analyses K. Vogel et al. 10.5194/nhess-14-2605-2014
87. 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
88. Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network M. Aamir et al. 10.3390/s21082648
89. Comprehensive Understanding the Disaster-Causing Mechanism, Governance Dilemma and Targeted Countermeasures of Urban Pluvial Flooding in China F. Kong et al. 10.3390/w13131762
90. Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts R. Paulik et al. 10.3390/jmse11101960
91. Comparison of two modelling strategies for 2D large-scale flood simulations S. Dazzi et al. 10.1016/j.envsoft.2021.105225
92. Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China J. Zhao et al. 10.3389/feart.2023.1042088
93. Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models K. Vogel et al. 10.1029/2018WR022858
94. 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
95. 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
96. 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
97. Multi-Variate Analyses of Flood Loss in Can Tho City, Mekong Delta D. Chinh et al. 10.3390/w8010006
98. Probabilistic Flood Loss Models for Companies L. Schoppa et al. 10.1029/2020WR027649
99. Multi-variable flood damage modelling with limited data using supervised learning approaches D. Wagenaar et al. 10.5194/nhess-17-1683-2017
100. Flood Vulnerability Models and Household Flood Damage Mitigation Measures: An Econometric Analysis of Survey Data T. Endendijk et al. 10.1029/2022WR034192
101. Commercial Real-Estate at Risk: An Examination of Commercial Building and Economic Impacts in the United States Using a High-Precision Flood Risk Assessment Tool J. Porter et al. 10.3389/frwa.2022.875995
102. Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression C. Wang et al. 10.1007/s00477-016-1242-6
103. 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
104. Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes N. Sairam et al. 10.1029/2019WR025068
105. Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning C. Bowers et al. 10.1061/NHREFO.NHENG-1995
106. Mapping flood susceptibility in mountainous areas on a national scale in China G. Zhao et al. 10.1016/j.scitotenv.2017.10.037
107. 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
108. 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
109. 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
110. Multi-model ensembles for assessment of flood losses and associated uncertainty R. Figueiredo et al. 10.5194/nhess-18-1297-2018
111. 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
112. 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
113. Tracing the value of data for flood loss modelling K. Schröter et al. 10.1051/e3sconf/20160705005
114. Global-Scale Assessment of Economic Losses Caused by Flood-Related Business Interruption R. Taguchi et al. 10.3390/w14060967
115. 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
116. Adaptability and transferability of flood loss functions in residential areas H. Cammerer et al. 10.5194/nhess-13-3063-2013
117. Review Article: A comparison of flood and earthquake vulnerability assessment indicators M. de Ruiter et al. 10.5194/nhess-17-1231-2017
118. Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification T. Sieg et al. 10.1371/journal.pone.0212932
119. Prediction of Heavy Rain Damage Using Deep Learning K. Lee et al. 10.3390/w12071942
120. Predictive multi-hazard hurricane data-based fragility model for residential homes C. Massarra et al. 10.1016/j.coastaleng.2019.04.008
121. An Empirical, Functional approach to Depth Damages W. Lehman et al. 10.1051/e3sconf/20160705002
122. Validation of flood risk models: Current practice and possible improvements D. Molinari et al. 10.1016/j.ijdrr.2018.10.022
123. 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
124. 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
125. Quantitative study of storm surge risk assessment in an undeveloped coastal area of China based on deep learning and geographic information system techniques: a case study of Double Moon Bay L. Yu et al. 10.5194/nhess-24-2003-2024
126. 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
127. 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
128. 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
129. 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
130. A data-driven approach for regional-scale fine-resolution disaster impact prediction under tropical cyclones P. Lin & N. Wang 10.1007/s11069-024-06527-y
131. Improving predictions and management of hydrological extremes through climate services B. van den Hurk et al. 10.1016/j.cliser.2016.01.001
132. Toward an adequate level of detail in flood risk assessments T. Sieg et al. 10.1111/jfr3.12889
133. Significance of chloride penetration controlling parameters in concrete: Ensemble methods W. Taffese & E. Sistonen 10.1016/j.conbuildmat.2017.02.014
134. Tree‐based flood damage modeling of companies: Damage processes and model performance T. Sieg et al. 10.1002/2017WR020784
135. Regional Index Insurance Using Satellite‐Based Fractional Flooded Area B. Tellman et al. 10.1029/2021EF002418
136. Projected seasonal flooding in Canada under climate change with statistical and machine learning M. Grenier et al. 10.1016/j.ejrh.2024.101754
137. 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
138. 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
139. Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe D. Paprotny et al. 10.1016/j.scitotenv.2020.140011
140. Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. 10.1111/risa.13575
141. Construction and Application of Reservoir Flood Control Operation Rules Using the Decision Tree Algorithm Y. Diao et al. 10.3390/w13243654
142. Preface: Flood-risk analysis and integrated management P. Bubeck et al. 10.5194/nhess-16-1005-2016
143. Understanding the Costs of Inaction–An Assessment of Pluvial Flood Damages in Two European Cities H. Nicklin et al. 10.3390/w11040801
144. Modelling flood damages under climate change conditions – a case study for Germany F. Hattermann et al. 10.5194/nhess-14-3151-2014
145. Seamless Estimation of Hydrometeorological Risk Across Spatial Scales T. Sieg et al. 10.1029/2018EF001122
146. 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
147. Estimating exposure of residential assets to natural hazards in Europe using open data D. Paprotny et al. 10.5194/nhess-20-323-2020
148. Spatial Dependence Modeling of Flood Risk Using Max-Stable Processes: The Example of Austria H. Albrecher et al. 10.3390/w12061805
149. 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
150. INSYDE-BE: adaptation of the INSYDE model to the Walloon region (Belgium) A. Scorzini et al. 10.5194/nhess-22-1743-2022
151. Fluvial Flood Losses in the Contiguous United States Under Climate Change M. Rashid et al. 10.1029/2022EF003328
152. A multivariate copula‐based framework for dealing with hazard scenarios and failure probabilities G. Salvadori et al. 10.1002/2015WR017225
153. Estimation of river flood damages in Jakarta, Indonesia P. Wijayanti et al. 10.1007/s11069-016-2730-1
154. 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
155. Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach M. Museru et al. 10.1016/j.scitotenv.2023.167872
156. Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data T. Gerl et al. 10.3390/w6082367
157. Flood loss models for residential buildings, based on the 2013 Colorado floods D. Deniz et al. 10.1007/s11069-016-2615-3
158. 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
159. Exploring the potential of multivariate depth‐damage and rainfall‐damage models L. Van Ootegem et al. 10.1111/jfr3.12284
160. 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
161. 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