Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

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外文摘要：The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30-110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

外文关键词：

Landslide susceptibility predictionRandom landslide position errorsUncertainty analysisMulti-layer perceptronRandom forestSemi-supervised machine learning

作者：

Faming Huang、Ronghui Li、Filippo Catani、Xiaoting Zhou、Ziqiang Zeng、Jinsong Huang

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作者单位：

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

Jiangxi Provincial Key Laboratory of Interdisciplinary Science,Nanchang University,Nanchang,330031,China

Department of Geosciences,University of Padova,Via Gradenigo 6,Padova,35131,Italy

School of Architectural Engineering,Jiangxi Science and Technology Normal University,Nanchang,330031,China

Jiangxi Provincial Science and Technology Affairs Center,Nanchang,330006,China

Discipline of Civil,Surveying and Conditioning Engineering,Priority Research Centre for Geotechnical Science and Engineering,University of Newcastle,NSW,2207,Australia

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基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaInterdisciplinary Innovation Fund of Natural Science,Nanchang University

项目编号：

42377164520790629167-28220007-YB2107

出版年：

2024

DOI：

10.1016/j.jrmge.2024.02.001

岩石力学与岩土工程学报(英文版)

中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD

影响因子：0.404

ISSN：1674-7755

年,卷(期)：2024.16(10)