Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-807-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-807-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2021
Research article |
| 01 Mar 2021
| 01 Mar 2021
Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Antonia Sebastian
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Department of Geological Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
Russell Blessing
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Wesley E. Highfield
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Laura Stearns
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Samuel D. Brody
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
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47 citations as recorded by crossref.
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- Riverine flood potential assessment using metaheuristic hybrid machine learning algorithms M. Vojtek et al. 10.1111/jfr3.12905
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- Predicting road flooding risk with crowdsourced reports and fine-grained traffic data F. Yuan et al. 10.1007/s43762-023-00082-1
- Applying Machine Learning Techniques to Identify Key Factors Motivating Flood-Prone Residents to Implement Private Flood Mitigation Measures A. Fouladi Semnan et al. 10.1061/NHREFO.NHENG-1928
- Critical zone controls on stream chemistry: Lessons from multiple machine learning methods and irregular data across large watersheds G. Goldrich-Middaugh et al. 10.1016/j.jhydrol.2025.133319
- Advancing Coastal Flood Risk Prediction Utilizing a GeoAI Approach by Considering Mangroves as an Eco-DRR Strategy T. Atmaja et al. 10.3390/hydrology11120198
- Reconceptualizing urban flood modeling: Toward intersectional and equitable urban flood risk planning M. Scolio et al. 10.1016/j.scs.2025.106578
- Multimodal Mamba with multitask learning for building flood damage assessment using synthetic aperture radar remote sensing imagery Y. Ho & A. Mostafavi 10.1111/mice.70059
- Critical facility accessibility and road criticality assessment considering flood-induced partial failure U. Gangwal et al. 10.1080/23789689.2022.2149184
- Modelling and Mapping Rapid-Onset Coastal Flooding: A Systematic Literature Review A. Re et al. 10.3390/w17040599
- Leveraging mesh modularization to lower the computational cost of localized updates to regional 2D hydrodynamic model outputs M. Garcia et al. 10.1080/19942060.2023.2225584
- Exploring the relationship between flood insurance claims, crowdsourced rainfall, and tide levels for coastal urban communities: Case study for the mid-Atlantic United States A. Chen et al. 10.1016/j.jhydrol.2023.130123
- Delineating Urban Flooding When Incorporating Community Stormwater Knowledge M. Scolio et al. 10.2139/ssrn.4758102
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- Multi-Hazard property buyouts: Making a case for the acquisition of flood and contaminant-prone residential properties in Galena Park, TX K. Atoba et al. 10.1016/j.crm.2023.100529
- Measuring climate change from an actuarial perspective: A survey of insurance applications N. Zhou et al. 10.1111/1758-5899.13465
- Satellite Video Remote Sensing for Flood Model Validation C. Masafu & R. Williams 10.1029/2023WR034545
- Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco M. El baida et al. 10.1007/s11069-024-06596-z
- Optimal Operation of Floodwater Resources Utilization of Lakes in South-to-North Water Transfer Eastern Route Project Z. Yang et al. 10.3390/su13094857
- Spatially estimating flooding depths from damage reports L. Haselbach et al. 10.1007/s11069-023-05921-2
- A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping M. El baida et al. 10.1007/s11269-024-03940-7
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- Multi-hazard exposure mapping under climate crisis using random forest algorithm for the Kalimantan Islands, Indonesia S. Heo et al. 10.1038/s41598-023-40106-8
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Latest update: 17 Sep 2025
Short summary
In southeast Texas, flood impacts are exacerbated by increases in impervious surfaces, human inaction, outdated FEMA-defined floodplains and modeling assumptions, and changing environmental conditions. The current flood maps are inadequate indicators of flood risk, especially in urban areas. This study proposes a novel method to model flood hazard and impact in urban areas. Specifically, we used novel flood risk modeling techniques to produce annualized flood hazard maps.
In southeast Texas, flood impacts are exacerbated by increases in impervious surfaces, human...
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