Abstract
AbstractArtificial levees are anthropogenic structures designed to hydrologically disconnect rivers from floodplains. The extent of artificial levees in the contiguous United States (CONUS) is unknown. To better estimate the distribution of artificial levees, we tested several different geomorphic, land cover, and spatial variables developed from the National Elevation Dataset, the National Land Cover database, and the National Hydrology Dataset HR Plus. We used known levee locations from the National Levee Database as training data. We tested machine learning and general logistic models’ ability to detect artificial levees in a 100‐year hydrogeomorphic floodplain of seven geographically diverse 8‐digit HUC basins. Random forest models outperformed other models in predicting the location of levees using variables representing geomorphic attributes, land cover, and distance from streams ranging in size between stream order one through six. To demonstrate the ability of our approach to detect unknown levees, we conducted a leave‐one‐out cross‐validation in the lower Mississippi Basin using approximately 1,100 artificial levees. This approach detected known levees constituting 94% of the total levee length in the basin. Scaling up to the CONUS, we applied a high performing (overall accuracy of 97%) random forest model using land cover and stream order variables. We detected 182,213 km of potential levees, mostly along streams of order 2–6 in the Mississippi and Missouri River Basins, indicating that the national levee database contains 20.4% of levee length. Potential levees and those documented in the national levee database modify 2% of the total length of streams in the contiguous United States.
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