55 citations as recorded by crossref.

1. Analysis of pluvial flood damage costs in residential buildings – A case study in Malmö S. Mobini et al. https://doi.org/10.1016/j.ijdrr.2021.102407
2. Characterizing precipitation events leading to surface water flood damage over large regions of complex terrain D. Bernet et al. https://doi.org/10.1088/1748-9326/ab127c
3. On the occurrence of rainstorm damage based on home insurance and weather data M. Spekkers et al. https://doi.org/10.5194/nhess-15-261-2015
4. Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. https://doi.org/10.5194/nhess-20-1149-2020
5. Insurance risk assessment in the face of climate change: Integrating data science and statistics V. Lyubchich et al. https://doi.org/10.1002/wics.1462
6. Multi-variable flood damage modelling with limited data using supervised learning approaches D. Wagenaar et al. https://doi.org/10.5194/nhess-17-1683-2017
7. Can urban pluvial flooding be predicted by open spatial data and weather data? S. Gaitan et al. https://doi.org/10.1016/j.envsoft.2016.08.007
8. Surface water floods in Switzerland: what insurance claim records tell us about the damage in space and time D. Bernet et al. https://doi.org/10.5194/nhess-17-1659-2017
9. Fluvial Flood Losses in the Contiguous United States Under Climate Change M. Rashid et al. https://doi.org/10.1029/2022EF003328
10. Study on Influencing Factors of Flood Damage to Single Houses using Field Survey Data C. Choi et al. https://doi.org/10.9798/KOSHAM.2018.18.7.535
11. The impact of intense rainfall on insurance losses in two Swedish cities B. Blumenthal & L. Nyberg https://doi.org/10.1111/jfr3.12504
12. A review of geospatial-based urban growth models and modelling initiatives S. Musa et al. https://doi.org/10.1080/10106049.2016.1213891
13. Multivariate pluvial flood damage models L. Van Ootegem et al. https://doi.org/10.1016/j.eiar.2015.05.005
14. Drivers of basement flooding: Rainfall intensity and infrastructure impacts in Trelleborg, Sweden S. Mobini et al. https://doi.org/10.1016/j.ejrh.2026.103512
15. Evaluating Flood Exposure for Properties in Urban Areas Using a Multivariate Modelling Technique G. Torgersen et al. https://doi.org/10.3390/w9050318
16. Hidden semi-Markov models for rainfall-related insurance claims Y. Shi et al. https://doi.org/10.1016/j.insmatheco.2024.11.008
17. Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates V. Rözer et al. https://doi.org/10.1029/2018EF001074
18. Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar W. Zin et al. https://doi.org/10.20965/jdr.2020.p0300
19. Assessment of the Physical Vulnerability of Vernacular Architecture to Meteorological Hazards Using an Indicator-Based Approach: The Case of the Kara Region in Northern Togo M. Awoussi et al. https://doi.org/10.3390/buildings15132249
20. A Disparate Disaster: Spatial Patterns of Building Damage Caused by Hurricane Ian and Associated Socio-Economic Factors M. Salim et al. https://doi.org/10.3390/rs16203792
21. Residential flood loss estimated from Bayesian multilevel models G. Mohor et al. https://doi.org/10.5194/nhess-21-1599-2021
22. Spatial Distribution of Flood Incidents Along Urban Overland Flow-Paths S. Gaitan et al. https://doi.org/10.1007/s11269-015-1006-y
23. Assessing present and future risk of water damage using building attributes, meteorology, and topography* C. Heinrich-Mertsching et al. https://doi.org/10.1093/jrsssc/qlad043
24. Karar Ağacı Yaklaşımı ile Doğal ve Geri Dönüştürülmüş Beton Agreganın Mesafe Bazlı Maliyet ve Emisyon Optimizasyonu M. Akbaş & R. İyisan https://doi.org/10.54365/adyumbd.1602399
25. Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks S. Gaitan & J. ten Veldhuis https://doi.org/10.5194/piahs-370-9-2015
26. Validation of flood risk models: Current practice and possible improvements D. Molinari et al. https://doi.org/10.1016/j.ijdrr.2018.10.022
27. Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy) F. Carisi et al. https://doi.org/10.5194/nhess-18-2057-2018
28. The use of insurance data in the analysis of Surface Water Flood events – A systematic review K. Gradeci et al. https://doi.org/10.1016/j.jhydrol.2018.10.060
29. Urban flood damage claim analyses for improved flood damage assessment S. Mobini et al. https://doi.org/10.1016/j.ijdrr.2022.103099
30. Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area A. Caldas-Alvarez et al. https://doi.org/10.5194/nhess-22-3701-2022
31. A generic physical vulnerability model for floods: review and concept for data-scarce regions M. Malgwi et al. https://doi.org/10.5194/nhess-20-2067-2020
32. Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam C. Luu et al. https://doi.org/10.1007/s11069-021-04821-7
33. Developing drought impact functions for drought risk management S. Bachmair et al. https://doi.org/10.5194/nhess-17-1947-2017
34. Proxy Data of Surface Water Floods in Rural Areas: Application to the Evaluation of the IRIP Intense Runoff Mapping Method Based on Satellite Remote Sensing and Rainfall Radar A. Cerbelaud et al. https://doi.org/10.3390/w14030393
35. 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. https://doi.org/10.1016/j.cscm.2024.e03004
36. A comparative survey of the impacts of extreme rainfall in two international case studies M. Spekkers et al. https://doi.org/10.5194/nhess-17-1337-2017
37. Understanding the Costs of Inaction–An Assessment of Pluvial Flood Damages in Two European Cities H. Nicklin et al. https://doi.org/10.3390/w11040801
38. Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. https://doi.org/10.5194/nhess-19-661-2019
39. Flood loss estimation using 3D city models and remote sensing data K. Schröter et al. https://doi.org/10.1016/j.envsoft.2018.03.032
40. Developing a multivariable lookup table function for estimating flood damages of rice crop in Vietnam using a secondary research approach N. Nguyen et al. https://doi.org/10.1016/j.ijdrr.2021.102208
41. Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression C. Wang et al. https://doi.org/10.1007/s00477-016-1242-6
42. Towards Sustainable and Resilient Infrastructure: Hurricane-Induced Roadway Closure and Accessibility Assessment in Florida Using Machine Learning S. Takyi et al. https://doi.org/10.3390/su17093909
43. GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam C. Luu et al. https://doi.org/10.1016/j.jhydrol.2021.126500
44. Identification of Rainfall Thresholds Likely to Trigger Flood Damages across a Mediterranean Region, Based on Insurance Data and Rainfall Observations K. Papagiannaki et al. https://doi.org/10.3390/w14060994
45. Flood Damage Analysis Using Machine Learning Techniques . Snehil & R. Goel https://doi.org/10.1016/j.procs.2020.06.011
46. Toward an adequate level of detail in flood risk assessments T. Sieg et al. https://doi.org/10.1111/jfr3.12889
47. Modeling the extent of surface water floods in rural areas: Lessons learned from the application of various uncalibrated models D. Bernet et al. https://doi.org/10.1016/j.envsoft.2018.08.005
48. Development of a flood damage mitigation index for flood management capacity assessment using regression function parameters H. Park & S. Seo https://doi.org/10.1007/s11069-025-07605-5
49. Empirical causal analysis of flood risk factors on U.S. flood insurance payouts:Implications for solvency and risk reduction A. Bhattacharyya & M. Hastak https://doi.org/10.1016/j.jenvman.2024.120075
50. Assessing the dependence between extreme rainfall and extreme insurance claims: A bivariate peak over threshold method Y. Shi https://doi.org/10.1111/risa.70033
51. Understanding flood risk in the context of community resilience modeling for the built environment: research needs and trends O. Nofal & J. van de Lindt https://doi.org/10.1080/23789689.2020.1722546
52. Assessing the likelihood of drought impact occurrence with extreme gradient boosting: a case study on the public water supply in South Korea J. Seo & Y. Kim https://doi.org/10.2166/hydro.2023.064
53. Vulnerability of Buildings to Meteorological Hazards: A Web-Based Application Using an Indicator-Based Approach M. Papathoma-Köhle et al. https://doi.org/10.3390/app13106253
54. Assessing effects of nature-based and other municipal adaptation measures on insured heavy rain damages V. Ooms et al. https://doi.org/10.5194/nhess-25-4283-2025
55. Coping with Pluvial Floods by Private Households V. Rözer et al. https://doi.org/10.3390/w8070304