Uncertainties in landslide susceptibility prediction modeling:A review on the incompleteness of landslide inventory and its influence rules

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外文摘要：Landslide inventory is an indispensable output variable of landslide susceptibility prediction(LSP)mod-elling.However,the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored.Adopting Xunwu County,China,as an example,the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions,after which different landslide inventory sample missing conditions are simulated by random sampling.It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10％,20％,30％,40％and 50％,as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation.Then,five machine learning models,namely,Random Forest(RF),and Support Vector Machine(SVM),are used to perform LSP.Finally,the LSP results are evaluated to analyze the LSP uncertainties under various con-ditions.In addition,this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions.Results show that(1)randomly missing landslide inventory samples at certain proportions(10％-50％)may affect the LSP results for local areas.(2)Aggregation of missing landslide inventory samples may cause significant biases in LSP,particularly in areas where samples are missing.(3)When 50％of landslide samples are missing(either randomly or aggregated),the changes in the decision basis of the RF model are mainly manifested in two aspects:first,the importance ranking of environmental factors slightly differs;second,in regard to LSP modelling in the same test grid unit,the weights of individual model factors may dras-tically vary.

外文关键词：

Landslide susceptibility predictionLandslide inventoryMachine learning interpretabilitySHapley additive explanationsPartial dependence plot

作者：

Faming Huang、Daxiong Mao、Shui-Hua Jiang、Chuangbing Zhou、Xuanmei Fan、Ziqiang Zeng、Filippo Catani、Changshi Yu、Zhilu Chang、Jinsong Huang、Bingchen Jiang、Yijing Li

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作者单位：

School of Infrastructure Engineering,Nanchang University,Nanchang 330031,China

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu,Sichuan 610059,China

Science and Technology Department of Jiangxi Province,Nanchang 330046,China

Department of Geosciences,University of Padova,Padova,Italy

Information Engineering School,Nanchang University,Nanchang 330031,China

Discipline of Civil,Surveying and Conditioning Engineering,Priority Research Centre for Geotechnical Science and Engineering,University of Newcastle,NSW,Australia

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出版年：

2024

DOI：

10.1016/j.gsf.2024.101886

地学前缘(英文版)

中国地质大学（北京）北京大学

地学前缘(英文版)

CSTPCD

影响因子：0.576

ISSN：1674-9871

年,卷(期)：2024.15(6)