Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas
<p>Pre-disaster planning and mitigation necessitate detailed spatial information about flood hazards and their associated risks. In the US, the Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) provides important information about areas subject to flooding during the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-03-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/21/807/2021/nhess-21-807-2021.pdf |
| _version_ | 1818944936936996864 |
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| author | W. Mobley A. Sebastian A. Sebastian R. Blessing W. E. Highfield L. Stearns S. D. Brody |
| author_facet | W. Mobley A. Sebastian A. Sebastian R. Blessing W. E. Highfield L. Stearns S. D. Brody |
| author_sort | W. Mobley |
| collection | DOAJ |
| description | <p>Pre-disaster planning and mitigation necessitate detailed spatial information about flood hazards and their associated risks. In the US, the Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) provides important information about areas subject to flooding during the 1 <span class="inline-formula"><i>%</i></span> riverine or coastal event. The binary nature of flood hazard maps obscures the distribution of property risk inside of the SFHA and the residual risk outside of the SFHA, which can undermine mitigation efforts. Machine learning techniques provide an alternative approach to estimating flood hazards across large spatial scales at low computational expense. This study presents a pilot study for the Texas Gulf Coast region using random forest classification to predict flood probability across a 30 523 km<span class="inline-formula"><sup>2</sup></span> area. Using a record of National Flood Insurance Program (NFIP) claims dating back to 1976 and high-resolution geospatial data, we generate a continuous flood hazard map for 12 US Geological Survey (USGS) eight-digit hydrologic unit code (HUC) watersheds. Results indicate that the random forest model predicts flooding with a high sensitivity (area under the curve, AUC: 0.895), especially compared to the existing FEMA regulatory floodplain. Our model identifies 649 000 structures with at least a 1 <span class="inline-formula"><i>%</i></span> annual chance of flooding, roughly 3 times more than are currently identified by FEMA as flood-prone.</p> |
| first_indexed | 2024-12-20T07:51:09Z |
| format | Article |
| id | doaj.art-10efcacf3eee49b587dce6f0f781885b |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-12-20T07:51:09Z |
| publishDate | 2021-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-10efcacf3eee49b587dce6f0f781885b2022-12-21T19:47:49ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812021-03-012180782210.5194/nhess-21-807-2021Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast TexasW. Mobley0A. Sebastian1A. Sebastian2R. Blessing3W. E. Highfield4L. Stearns5S. D. Brody6Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USADepartment of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USADepartment of Geological Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USADepartment of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USADepartment of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USADepartment of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USADepartment of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA<p>Pre-disaster planning and mitigation necessitate detailed spatial information about flood hazards and their associated risks. In the US, the Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) provides important information about areas subject to flooding during the 1 <span class="inline-formula"><i>%</i></span> riverine or coastal event. The binary nature of flood hazard maps obscures the distribution of property risk inside of the SFHA and the residual risk outside of the SFHA, which can undermine mitigation efforts. Machine learning techniques provide an alternative approach to estimating flood hazards across large spatial scales at low computational expense. This study presents a pilot study for the Texas Gulf Coast region using random forest classification to predict flood probability across a 30 523 km<span class="inline-formula"><sup>2</sup></span> area. Using a record of National Flood Insurance Program (NFIP) claims dating back to 1976 and high-resolution geospatial data, we generate a continuous flood hazard map for 12 US Geological Survey (USGS) eight-digit hydrologic unit code (HUC) watersheds. Results indicate that the random forest model predicts flooding with a high sensitivity (area under the curve, AUC: 0.895), especially compared to the existing FEMA regulatory floodplain. Our model identifies 649 000 structures with at least a 1 <span class="inline-formula"><i>%</i></span> annual chance of flooding, roughly 3 times more than are currently identified by FEMA as flood-prone.</p>https://nhess.copernicus.org/articles/21/807/2021/nhess-21-807-2021.pdf |
| spellingShingle | W. Mobley A. Sebastian A. Sebastian R. Blessing W. E. Highfield L. Stearns S. D. Brody Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas Natural Hazards and Earth System Sciences |
| title | Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas |
| title_full | Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas |
| title_fullStr | Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas |
| title_full_unstemmed | Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas |
| title_short | Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas |
| title_sort | quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales a pilot study in southeast texas |
| url | https://nhess.copernicus.org/articles/21/807/2021/nhess-21-807-2021.pdf |
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