Uncertainties of landslide susceptibility prediction considering different landslide types

Most literature related to landslide susceptibility prediction only considers a single type of landslide, such as colluvial landslide, rock fall or debris flow, rather than different landslide types, which greatly affects susceptibility prediction performance. To construct efficient susceptibility prediction considering different landslide types, Huichang County in China is taken as example. Firstly, 105 rock falls, 350 colluvial landslides and 11 related environmental factors are identified. Then four machine learning models, namely logistic regression, multi-layer perception, support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide. Thirdly, three different landslide susceptibility prediction (LSP) models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility: (i) united method, which combines all landslide types directly; (ii) probability statistical method, which couples analyses of susceptibility indices under different landslide types based on probability formula; and (iii) maximum comparison method, which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides. Finally, uncertainties of landslide susceptibility are assessed by prediction accuracy, mean value and standard deviation. It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County. The united method has the best susceptibility prediction performance, followed by the probability method and maximum susceptibility method. More cases are needed to verify this result in-depth. LSP considering different landslide types is superior to that taking only a single type of landslide into account. (C) 2023 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).