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Probabilistic slope failure occurrence models were created using LiDAR-derived terrain variables and user-defined landslide points. These models were created as part of a multihazard risk assessment for West Virginia through FEMA's Hazard Mitigation Grant Program (HMGP). Statewide models created for each Major Land Resource Area (MLRA) have been published to both the West Virginia Flood Tool and West Virginia Landslide Tool and are accessible to the public. |
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Probabilistic slope failure occurrence models were created using LiDAR-derived terrain variables and user-defined landslide points. These models were created as part of a multihazard risk assessment for West Virginia through FEMA's Hazard Mitigation Grant Program (HMGP). Statewide models created for each Major Land Resource Area (MLRA) have been published to both the West Virginia Flood Tool and West Virginia Landslide Tool and are accessible to the public. |
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WVGISTC (West Virginia GIS Technical Center), WVU (West Virginia University) |
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5000 |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Slope failure probabilistic models were generated using random forest machine learning methods, user-identified landslide incidence points, and LiDAR digital terrain variables. The nature of the West Virginia landscape and the LiDAR imagery limited mapping of user-identified landslide incidence points to landslides at least 33 feet wide. This approach undercounts small, shallow landslides and slope failures that may have been mitigated or removed by human agents. Models were trained and validated in R using both presence data (user-defined points) and absence data (randomly generated pseudo absence non-landslide points). For a detailed description of methods and data layers, the following open-source articles can be accessed:</SPAN></P><P><SPAN>Maxwell, A.E.; Sharma, M.; Kite, J.S.; Donaldson, K.A.; Thompson, J.A.; Bell, M.L.; Maynard, S.M. Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt. Remote Sens. 2020, 12, 486. https://doi.org/10.3390/rs12030486</SPAN></P><P><SPAN>Maxwell, A.E.; Sharma, M.; Kite, J.S.; Donaldson, K.A.; Maynard, S.M.; Malay, C.M. Assessing the Generalization of Machine Learning-Based Slope Failure Prediction to New Geographic Extents. ISPRS Int. J. Geo-Inf. 2021, 10, 293. https://doi.org/10.3390/ijgi10050293</SPAN></P><P><SPAN>These models, which exist as a landslide susceptibility index from 0 to 1 in raster grid form, have been classified using the following classes:</SPAN></P><P><SPAN>0.0-0.3 - low susceptibility</SPAN></P><P><SPAN>0.3-0.7 - moderate susceptibility</SPAN></P><P><SPAN>0.7-1.0 - high susceptibility</SPAN></P></DIV></DIV></DIV> |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Landslide susceptibility classifications used in this risk assessment are based on landslide locations mapped using LiDAR data. The mapping procedure and data used makes detecting landslides difficult in some areas, particularly areas where landslide material is removed shortly after the failure occurs (i.e. roads, populated areas, stream banks). The nature of the West Virginia landscape and the LiDAR imagery limited mapping of user-identified landslide incidence points to landslides at least 33 feet wide. This approach undercounts small, shallow landslides and slope failures that may have been mitigated or removed by human agents. LiDAR data processing also makes detecting landslides difficult in highly developed areas (i.e. housing developments, industrial parks). LiDAR-mapped landslide locations and landslide susceptibility maps derived from that data are inherently biased against these areas. </SPAN></P><P><SPAN>Landslide risk estimates are approximate. Landslide risk assessments conducted by licensed engineers and qualified geologists may vary from estimated risk and the associated costs. Landslide susceptibility estimates calculated in this dataset are based on the best available asset data, which may be inaccurate. Future studies, landscape alteration, mapping efforts, and asset data may render these results inaccurate. </SPAN></P><P><SPAN>This study is for informational purposes related to general emergency services planning. It has not been prepared for, and may not be suitable for legal, design, engineering, or site-preparation purposes. This susceptibility grid cannot substitute for site-specific investigations by qualified practitioners. Landslide risk is complex and continually changing. Although other existing studies or reports may provide more precise and comprehensive information, detailed original site investigations are normally an essential best practice for public safety, sustainability, and financial viability. These other data sources may give results that differ from those in this dataset.</SPAN></P></DIV></DIV></DIV> |
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Statewide_LandslideHazSusceptibility_20231109.tif |
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["Landslide Susceptibility Grid","West Virginia","Landslide","Susceptibility"] |
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en-US |
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150000000 |
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