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TitleLandslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada
AuthorBehnia, P; Blais-Stevens, A
SourceNatural Hazards vol. 90, 3, 2018 p. 1407-1426, https://doi.org/10.1007/s11069-017-3104-z
Year2018
Alt SeriesNatural Resources Canada, Contribution Series 20182057
PublisherSpringer Netherlands
Documentserial
Lang.English
Mediapaper; on-line; digital
ProvinceYukon
ProgramMarine Geohazards, Public Safety Geoscience
Released2017 11 10
AbstractThe random forest method was used to generate susceptibility maps for debris flows, rock slides, and active layer detachment slides in the Donjek River area within the Yukon Alaska Highway Corridor, based on an inventory of landslides compiled by the Geological Survey of Canada in collaboration with the Yukon Geological Survey. The aim of this study is to develop data-driven landslide susceptibility models which can provide information on risk assessment to existing and planned infrastructure. The factors contributing to slope failure used in the models include slope angle, slope aspect, plan and profile curvatures, bedrock geology, surficial geology, proximity to faults, permafrost distribution, vegetation distribution, wetness index, and proximity to drainage system. A total of 83 debris flow deposits, 181 active layer detachment slides, and 104 rock slides were compiled in the landslide inventory. The samples representing the landslide free zones were randomly selected. The ratio of landslide/landslide free zones was set to 1:1 and 1:2 to examine the results of different sample ratios on the classification. Two-thirds of the samples for each landslide type were used in the classification, and the remaining 1/3 were used to evaluate the results. In addition to the classification maps, probability maps were also created, which served as the susceptibility maps for debris flows, rock slides, and active layer detachment slides. Success and prediction rate curves created to evaluate the performance of the resulting models indicate a high performance of the random forest in landslide susceptibility modelling. © 2017, Springer Science+Business Media B.V., part of Springer Nature.
GEOSCAN ID310603