首页|Uncertainties of landslide susceptibility prediction:Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

Uncertainties of landslide susceptibility prediction:Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5％,10％,15％ and 20％ are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5％to 20％,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Landslide susceptibility predictionConditioning factor errorsLow-pass filter methodMachine learning modelsInterpretability analysis

Faming Huang、Zuokui Teng、Chi Yao、Shui-Hua Jiang、Filippo Catani、Wei Chen、Jinsong Huang

展开 >

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

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

Department of Geosciences,University of Padova,Padova,Italy

College of Geology and Environment,Xi'an University of Science and Technology,Xi'an,710054,China

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

展开 >

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Science Fund for Distinguished Young Scholars of China

423771645207906252222905

2024

10.1016/j.jrmge.2023.11.001

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(1)
  • 1