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Open AccessArticle

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

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Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, USA
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West Virginia GIS Technical Center, Morgantown, WV 26505, USA
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Division of Plant and Soil Sciences, West Virginia University, Morgantown, WV 26506, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 486; https://doi.org/10.3390/rs12030486
Received: 10 January 2020 / Revised: 28 January 2020 / Accepted: 30 January 2020 / Published: 3 February 2020
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
The probabilistic mapping of landslide occurrence at a high spatial resolution and over a large geographic extent is explored using random forests (RF) machine learning; light detection and ranging (LiDAR)-derived terrain variables; additional variables relating to lithology, soils, distance to roads and streams and cost distance to roads and streams; and training data interpreted from high spatial resolution LiDAR-derivatives. Using a large training set and all predictor variables, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.946 is obtained. Our findings highlight the value of a large training dataset, the incorporation of a variety of terrain variables and the use of variable window sizes to characterize the landscape at different spatial scales. We also document important variables for mapping slope failures. Our results suggest that feature selection is not required to improve the RF modeling results and that incorporating multiple models using different pseudo absence samples is not necessary. From our findings and based on a review of prior studies, we make recommendations for high spatial resolution, large-area slope failure probabilistic mapping. View Full-Text
Keywords: slope failures; landslides; light detection and ranging; LiDAR; digital terrain analysis; machine learning; random forest; spatial predictive modeling slope failures; landslides; light detection and ranging; LiDAR; digital terrain analysis; machine learning; random forest; spatial predictive modeling
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MDPI and ACS Style

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.

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