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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Cited
34 citations as recorded by crossref.
- Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning C. Bowers et al. 10.1061/NHREFO.NHENG-1995
- 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
- Analysis of Sewer Network Performance in the Context of Modernization: Modeling, Sensitivity, and Uncertainty Analysis B. Szeląg et al. 10.1061/(ASCE)WR.1943-5452.0001610
- 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
- Theoretical Boundaries of Annual Flood Risk for Single-Family Homes Within the 100-Year Floodplain A. Al Assi et al. 10.1007/s41742-024-00577-7
- 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
- Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction H. Ghaedi et al. 10.1371/journal.pone.0271230
- Learning inter-annual flood loss risk models from historical flood insurance claims J. Salas et al. 10.1016/j.jenvman.2023.118862
- Unraveling the Temporal Importance of Community-Scale Human Activity Features for Rapid Assessment of Flood Impacts F. Yuan et al. 10.1109/ACCESS.2021.3137651
- Multi-hazard assessment for flood and Landslide risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's new capital S. Heo et al. 10.1016/j.heliyon.2024.e37789
- Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms X. Liu et al. 10.3390/ijerph192416544
- A data-driven spatial approach to characterize the flood hazard R. Mostafiz et al. 10.3389/fdata.2022.1022900
- Riverine flood potential assessment using metaheuristic hybrid machine learning algorithms M. Vojtek et al. 10.1111/jfr3.12905
- Empirical causal analysis of flood risk factors on U.S. flood insurance payouts:Implications for solvency and risk reduction A. Bhattacharyya & M. Hastak 10.1016/j.jenvman.2024.120075
- Assessing the compound flood risk in coastal areas: Framework formulation and demonstration M. Mitu et al. 10.1016/j.jhydrol.2023.130278
- Investigating ways to better communicate flood risk: the tight coupling of perceived flood map usability and accuracy K. Stephens et al. 10.1080/17477891.2023.2224956
- Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding F. Yuan et al. 10.1016/j.compenvurbsys.2022.101870
- 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
- Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment C. Singha et al. 10.1007/s11356-024-34286-7
- Preliminary risk assessment of regional industrial enterprise sites based on big data Y. Jiang et al. 10.1016/j.scitotenv.2022.156609
- Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features Z. Liu et al. 10.1016/j.compenvurbsys.2024.102096
- Critical facility accessibility and road criticality assessment considering flood-induced partial failure U. Gangwal et al. 10.1080/23789689.2022.2149184
- Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk J. ZHU et al. 10.1016/j.scs.2024.105984
- 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
- Unveiling spatial patterns of disaster impacts and recovery using credit card transaction fluctuations F. Yuan et al. 10.1177/23998083221090246
- Predicting flood damage probability across the conterminous United States E. Collins et al. 10.1088/1748-9326/ac4f0f
- 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
- Systemic Financial Risk Arising From Residential Flood Losses H. Thomson et al. 10.1029/2022EF003206
33 citations as recorded by crossref.
- Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning C. Bowers et al. 10.1061/NHREFO.NHENG-1995
- 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
- Analysis of Sewer Network Performance in the Context of Modernization: Modeling, Sensitivity, and Uncertainty Analysis B. Szeląg et al. 10.1061/(ASCE)WR.1943-5452.0001610
- 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
- Theoretical Boundaries of Annual Flood Risk for Single-Family Homes Within the 100-Year Floodplain A. Al Assi et al. 10.1007/s41742-024-00577-7
- 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
- Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction H. Ghaedi et al. 10.1371/journal.pone.0271230
- Learning inter-annual flood loss risk models from historical flood insurance claims J. Salas et al. 10.1016/j.jenvman.2023.118862
- Unraveling the Temporal Importance of Community-Scale Human Activity Features for Rapid Assessment of Flood Impacts F. Yuan et al. 10.1109/ACCESS.2021.3137651
- Multi-hazard assessment for flood and Landslide risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's new capital S. Heo et al. 10.1016/j.heliyon.2024.e37789
- Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms X. Liu et al. 10.3390/ijerph192416544
- A data-driven spatial approach to characterize the flood hazard R. Mostafiz et al. 10.3389/fdata.2022.1022900
- Riverine flood potential assessment using metaheuristic hybrid machine learning algorithms M. Vojtek et al. 10.1111/jfr3.12905
- Empirical causal analysis of flood risk factors on U.S. flood insurance payouts:Implications for solvency and risk reduction A. Bhattacharyya & M. Hastak 10.1016/j.jenvman.2024.120075
- Assessing the compound flood risk in coastal areas: Framework formulation and demonstration M. Mitu et al. 10.1016/j.jhydrol.2023.130278
- Investigating ways to better communicate flood risk: the tight coupling of perceived flood map usability and accuracy K. Stephens et al. 10.1080/17477891.2023.2224956
- Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding F. Yuan et al. 10.1016/j.compenvurbsys.2022.101870
- 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
- Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment C. Singha et al. 10.1007/s11356-024-34286-7
- Preliminary risk assessment of regional industrial enterprise sites based on big data Y. Jiang et al. 10.1016/j.scitotenv.2022.156609
- Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features Z. Liu et al. 10.1016/j.compenvurbsys.2024.102096
- Critical facility accessibility and road criticality assessment considering flood-induced partial failure U. Gangwal et al. 10.1080/23789689.2022.2149184
- Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk J. ZHU et al. 10.1016/j.scs.2024.105984
- 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
- Unveiling spatial patterns of disaster impacts and recovery using credit card transaction fluctuations F. Yuan et al. 10.1177/23998083221090246
- Predicting flood damage probability across the conterminous United States E. Collins et al. 10.1088/1748-9326/ac4f0f
- 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
1 citations as recorded by crossref.
Latest update: 21 Nov 2024
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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