Landslide Prediction Validation in Western North Carolina After Hurricane Helene
Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The al...
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MDPI AG
2024-12-01
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| Series: | Geotechnics |
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| Online Access: | https://www.mdpi.com/2673-7094/4/4/64 |
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| author | Sophia Lin Shenen Chen Ryan A. Rasanen Qifan Zhao Vidya Chavan Wenwu Tang Navanit Shanmugam Craig Allan Nicole Braxtan John Diemer |
| author_facet | Sophia Lin Shenen Chen Ryan A. Rasanen Qifan Zhao Vidya Chavan Wenwu Tang Navanit Shanmugam Craig Allan Nicole Braxtan John Diemer |
| author_sort | Sophia Lin |
| collection | DOAJ |
| description | Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions. |
| format | Article |
| id | doaj-art-2e3f85e4f3614c38a9cac7d3bcd0ab02 |
| institution | Kabale University |
| issn | 2673-7094 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Geotechnics |
| spelling | doaj-art-2e3f85e4f3614c38a9cac7d3bcd0ab022024-12-27T14:28:25ZengMDPI AGGeotechnics2673-70942024-12-01441259128110.3390/geotechnics4040064Landslide Prediction Validation in Western North Carolina After Hurricane HeleneSophia Lin0Shenen Chen1Ryan A. Rasanen2Qifan Zhao3Vidya Chavan4Wenwu Tang5Navanit Shanmugam6Craig Allan7Nicole Braxtan8John Diemer9Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Earth, Environment and Geographical Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Earth, Environment and Geographical Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USADepartment of Earth, Environment and Geographical Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USAHurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions.https://www.mdpi.com/2673-7094/4/4/64landslidesbridge failureshurricane Helenerandom forest |
| spellingShingle | Sophia Lin Shenen Chen Ryan A. Rasanen Qifan Zhao Vidya Chavan Wenwu Tang Navanit Shanmugam Craig Allan Nicole Braxtan John Diemer Landslide Prediction Validation in Western North Carolina After Hurricane Helene Geotechnics landslides bridge failures hurricane Helene random forest |
| title | Landslide Prediction Validation in Western North Carolina After Hurricane Helene |
| title_full | Landslide Prediction Validation in Western North Carolina After Hurricane Helene |
| title_fullStr | Landslide Prediction Validation in Western North Carolina After Hurricane Helene |
| title_full_unstemmed | Landslide Prediction Validation in Western North Carolina After Hurricane Helene |
| title_short | Landslide Prediction Validation in Western North Carolina After Hurricane Helene |
| title_sort | landslide prediction validation in western north carolina after hurricane helene |
| topic | landslides bridge failures hurricane Helene random forest |
| url | https://www.mdpi.com/2673-7094/4/4/64 |
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