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

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 213-230页

作者： Faming Huang Zuokui Teng Chi Yao Shui-Hua Jiang Filippo Catani Wei Chen Jinsong Huang School of Infrastructure Engineering Nanchang UniversityNanchang330031China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu610059China Department of Geosciences University of PadovaPadovaItaly College of Geology and Environment Xi’an University of Science and TechnologyXi’an710054China Discipline of Civil Surveying and Conditioning EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleNewcastleNSWAustralia

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 *** 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 *** 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 ***,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter ***,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study *** typical machine learning models,*** perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP ***,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 *** conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random e

关键词： Landslide susceptibility prediction Conditioning factor errors Low-pass filter method Machine learning models interpretability analysis

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