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Uncertainties of landslide susceptibility prediction considering different landslide types

Journal of Rock Mechanics and Geotechnical Engineering 2023年第11期15卷 2954-2972页

作者： Faming Huang Haowen Xiong Chi Yao filippo catani Chuangbing Zhou Jinsong Huang School of Infrastructure Engineering Nanchang UniversityNanchang330031China Jiangxi Provincial Key Laboratory of Interdisciplinary Science Nanchang UniversityNanchang330031China Department of Geosciences University of PadovaPadovaItaly Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleNSW2287Australia

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 *** construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as ***,105 rock falls,350 colluvial landslides and 11 related environmental factors are *** 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 ***,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 ***,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard *** is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang *** united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility *** cases are needed to verify this result *** considering different landslide types is superior to that taking only a single type of landslide into account.

关键词： Landslide susceptibility Landslide type Rock fall Colluvial landslides Machine learning models

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

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第10期16卷 4177-4191页

作者： Faming Huang Ronghui Li filippo catani Xiaoting Zhou Ziqiang Zeng Jinsong Huang School of Infrastructure Engineering Nanchang UniversityNanchang330031China Jiangxi Provincial Key Laboratory of Interdisciplinary Science Nanchang UniversityNanchang330031China Department of Geosciences University of PadovaVia Gradenigo 6Padova35131Italy School of Architectural Engineering Jiangxi Science and Technology Normal UniversityNanchang330031China Jiangxi Provincial Science and Technology Affairs Center Nanchang330006China Discipline of Civil Surveying and Conditioning EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleNSW2207Australia

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial ***,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP *** 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 *** paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an *** 30e110 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 *** LSP uncertainties are analyzed by the LSP accuracy and distribution ***,a semi-supervised model is proposed to relieve the LSP *** 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.

关键词： Landslide susceptibility prediction Random landslide position errors Uncertainty analysis Multi-layer perceptron Random forest Semi-supervised machine learning

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Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第5期15卷 1127-1143页

作者： Zhilu Chang filippo catani Faming Huang Gengzhe Liu Sansar Raj Meena Jinsong Huang Chuangbing Zhou School of Infrastructure Engineering Nanchang UniversityNanchang330031China Department of Geosciences University of PadovaPadovaItaly School of Design University of PennsylvaniaPhiladelphiaPA19104USA Discipline of Civil Surveying and Environmental EngineeringCollege of EngineeringScience and EnvironmentThe University of NewcastleCallaghanNSW2308Australia

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning *** efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope ***,LSP modeling based on these slope units has not been ***,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP *** this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and ***,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are *** Chongyi County is selected as the case study and is divided into 53,055 slope *** original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal *** forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP ***,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance *** show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning *** is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensivel

关键词： Landslide susceptibility prediction(LSP) Slope unit Multi-scale segmentation method(MSS) Heterogeneity of conditioning factors Machine learning models

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Towards hydrometeorological thresholds of reservoir-induced landslide from subsurface strain observations

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Science China(Technological Sciences) 2024年第6期67卷 1907-1922页

作者： YE Xiao ZHU HongHu WANG Jia ZHENG WanJi ZHANG Wei SCHENATO Luca PASUTO Alessandro catani filippo School of Earth Sciences and Engineering Nanjing UniversityNanjing210023China Department of Geosciences University of PadovaPadova35131Italy School of Geosciences and Info-Physics Central South UniversityChangsha410083China Department of Information Engineering University of PadovaPadova35131Italy National Research Council-Research Institute for Geo-Hydrological Protection(CNR-IRPI) Padova35127Italy

Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir *** forecasting and early warning based on surface displacements have been widely ***,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide *** this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February *** spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation *** the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day *** identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time *** results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,57.9 mm and rainfall intensity>24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring *** on the shoulder of this landslide case,our study informs a practical and reliable path

关键词： slow-moving landslide fiber-optic monitoring subsurface strain hydrometeorological threshold extreme weather

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Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

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International Journal of Coal Science & Technology 2024年第2期11卷 143-172页

作者： Faming Huang Yu Cao Wenbin Li filippo catani Guquan Song Jinsong Huang Changshi Yu School of Civil Engineering and Architecture Nanchang UniversityNanchang 330031China Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring/School of Land Science and Space Planning Hebei GEO UniversityShijiazhuang 050031China Department of Geosciences University of PadovaPaduaItaly Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleCallaghanNSWAustralia School of Information Engineering Nanchang UniversityNanchang 330031China

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were *** mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation *** 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with ***,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),*** LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou ***,LSMs of Ruijin at various mapping unit scales are generated in *** and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP *** LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area *** course,attentions should also be paid to the broader representativeness of large study *** LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's *** significance of environmental factors exhibits an averaging trend as study area scale increases from small to *** importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

关键词： Landslide susceptibility prediction Uncertainty analysis Study areas scales Mapping unit scales Slope units Random forest

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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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Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

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International Journal of Coal Science & Technology 2023年第2期10卷 83-96页

作者： Faming Huang Haowen Xiong Shixuan Chen Zhitao Lv Jinsong Huang Zhilu Chang filippo catani School of Civil Engineering and Architecture Nanchang UniversityNanchang 330031China Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleCallaghanNSW 2287Australia Department of Geosciences University of PadovaPaduaItaly

The numerical simulation and slope stability prediction are the focus of slope disaster ***,machine learning models are commonly used in the slope stability ***,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature *** issues can affect the accuracy of slope stability ***,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope *** the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are *** on these characteristics,typical soil slopes are constructed using the Geo-Studio *** control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input ***,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control *** slope stability coefficient and its corresponding control factors is a slope *** a result,a total of 2160 training samples and 450 testing samples are *** sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global ***,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

关键词： Slope stability prediction Long short-term memory Deep learning Geo-Studio software Machine learning model

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An updating of landslide susceptibility prediction from the perspective of space and time

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Geoscience Frontiers 2023年第5期14卷 295-307页

作者： Zhilu Chang Faming Huang Jinsong Huang Shui-Hua Jiang Yuting Liu Sansar Raj Meena filippo catani School of Infrastructure Engineering Nanchang UniversityNanchang 330031China Department of Geosciences University of PaduaPaduaItaly Department of Civil Environmental and Architectural EngineeringUniversity of PaduaPadovaItaly Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleNSWAustralia

Due to the similarity of conditioning factors,the aggregation feature of landslides and the multi-temporal landslide inventory,the spatial and temporal effects of landslides need to be considered in landslide susceptibility prediction(LSP).The ignorance of this issue will result in some biases and time-invariance in landslide ***,a novel framework has been proposed to update landslide susceptibility by simultaneously considering the spatial and temporal effects of landslides at the regional *** this framework,the landslide inventory of Chongyi County has been divided into pre-and fresh-landslide *** to the LSP results predicted by the support vector machine(SVM)model using the slope unit-based conditioning factors and pre-landslide inventory,a normalized spatial distance index(NSDI)is calculated to quantitatively represent the spatial correlation between landslides and surrounding slope units to develop the SVM-NSDI ***,the SVM-Updating model,which incorporates the LSP results of the SVM-NSDI model and fresh-landslide inventory,could be developed to update the LSP ***,the confusion matrix,the area under the receiver operating characteristic curve(AUC)and frequency ratio(FR)accuracy are used to evaluate the prediction performance of the above LSP *** F1-score values of the SVM,SVM-NSDI and SVM-Updating models are 0.776,0.816 and 0.831,*** AUC values are 0.869,0.903 and 0.914 and the FR accuracies are 0.795,0.853 and *** can be concluded that landslide susceptibility is a time-variant variable,which can be updated by considering the spatial correlation between landslides and surrounding slope units as well as the temporal effects of multi-temporal landslide *** study provides a new framework to update landslide susceptibility over time and also provides more accurate LSP results for decisionmakers.

关键词： Landslide susceptibility updating Spatial effect Temporal effect Machine learning

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Uncertainties of landslide susceptibility prediction:Influences of different spatial resolutions,machine learning models and proportions of training and testing dataset

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Rock Mechanics Bulletin 2023年第1期2卷 65-81页

作者： Faming Huang Zuokui Teng Zizheng Guo filippo catani Jinsong Huang Jiangxi Provincial Key Laboratory of Interdisciplinary Science Nanchang UniversityNanchang330031China School of Infrastructure Engineering Nanchang UniversityNanchang330031China School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China Department of Geosciences University of PadovaPadovaItaly Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleNSWAustralia

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning *** Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were *** frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP *** results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling *** a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations *** sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.

关键词： Landslide susceptibility prediction Uncertainty analysis Machine learning models Conditioning factors Spatial resolution Proportions of training and testing dataset

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Statistical and environmental analyses for the definition of a regional rainfall threshold system for landslide triggering in Tuscany （Italy）

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Journal of Geographical Sciences 2012年第4期22卷 617-629页

作者： ROSI Ascanio SEGONI Samuele catani filippo CASAGLI Nicola Department of Earth Science University of Florence Florence Italy

The aim of this work is the determination of regional-scale rainfall thresholds for the triggering of landslides in the Tuscany Region （Italy）. The critical rainfall events related to the occurrence of 593 past landslides were characterized in terms of duration （D） and intensity （I） I and D values were plotted in a log-log diagram and a lower boundary was clearly noticeable： it was interpreted as a threshold representing the rainfall conditions associated to landsliding. That was also confirmed by a comparison with many literature thresholds, but at the same time it was clear that a similar threshold would be affected by a too large approximation to be effectively used for a regional warning system. Therefore, further analyses were performed differentiating the events on the basis of seasonality, magnitude, location, land use and lithology. None of these criteria led to discriminate among all the events different groups to be characterized by a specific and more effective threshold. This outcome could be interpreted as the demonstration that at regional scale the best results are obtained by the simplest approach, in our case an empirical black box model which accounts only for two rainfall parameters （I and D）. So a set of thresholds could be conveniently defined using a statistical approach： four thresholds corresponding to four severity levels were defined by means of the prediction interval technique and we developed a prototype warning system based on rainfall recordings or weather forecasts.

关键词： landslide threshold rainfall black box models prediction intervals

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