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Journal of Rock Mechanics and Geotechnical Engineering 2024年第3期16卷 877-894页

作者： Zizheng Guo Bixia Tian Yuhang Zhu Jun He Taili Zhang School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment Sanhe065201China Faculty of Engineering China University of GeosciencesWuhan430074China Nanjing Center China Geological SurveyMinistry of Natural ResourcesNanjing210016China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

关键词： landslide susceptibility Sampling strategy Machine learning Random forest China

Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis:Evidence from Shimla district of North-west Indian Himalayan region

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Journal of Mountain Science 2024年第7期21卷 2368-2393页

作者： SHARMA Aastha SAJJAD Haroon RAHAMAN Md Hibjur SAHA Tamal Kanti BHUYAN Nirsobha Department of Geography Faculty of SciencesJamia Millia IslamiaNew Delhi-110025India

The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural meas

关键词： landslide susceptibility Site-specific factors Machine learning models Hybrid ensemble learning Geospatial techniques Himalayan region

landslide susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit,Which is Better?

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Journal of Earth Science 2023年第2期34卷 386-397页

作者： Siyuan Ma Xiaoyi Shao Chong Xu Key Laboratory of Seismic and Volcanic Hazards Institute of GeologyChina Earthquake AdministrationBeijing 100029China National Institute of Natural Hazards Ministry of Emergency Management of ChinaBeijing 100085China Key Laboratory of Compound and Chained Natural Hazards Dynamics Ministry of Emergency Management of ChinaBeijing 100085China

Choice of appropriate mapping units is important in landslide susceptibility mapping(LSM).There are various possible units for this choice,while it remains unclear which one is better in performance.The purpose of this study is to make a quantitative comparison of two commonly-used units:slope-unit(SU)and raster-unit(RU)based on the landslides triggered by the 2013 Minxian,Gansu,China Mw5.9 earthquake.Ten landslide influencing factors were considered in this analysis.For each type of mapping units,the 70%samples were randomly selected and trained 20 times on the LR model,yielding 20 susceptibility maps,and the remaining 30%samples were tested for the accuracy of the modeling outcome.Different metrics,including the mean probability,model uncertainty,and model prediction skills,were used to evaluate the quality of the susceptibility maps.The results show that the resultant probability maps using two mapping units can largely predict the distribution of actual landslides,on which the high susceptibility area corresponds to the landslide-prone area.The AUC(area under curve)values,ranging from 0.8 to 0.86,show that the prediction ability of two mapping units is roughly the same.While comparing with the RU,the use of SU can lower the model uncertainties caused by the variation of training sets.We converted the RU-based assessment results into SU-based scheme.The results show that two assessment results are well fitted with good linear relationship,which implies that it is feasible to convert the RU-based landslide susceptibility mapping into the SU-based scheme.This analysis indicates that compared with the RU,the SU cannot improve the performance and accuracy of seismic landslide susceptibility mapping.

关键词： earthquake-triggered landslides Minxian earthquake logistic regression(LR)model landslide susceptibility mapping unit engineering geology

Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan

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山地科学学报(英文) 2023年第9期20卷 2603-2627页

作者： Aamir ASGHAR SU Li-jun ZHAO Bo Nadeem Ahmad USMANI Key Laboratory of Mountain Hazards and Earth Surface Process Institute of Mountain Hazards and EnvironmentChinese Academy of Sciences(CAS)Chengdu 610041China University of Chinese Academy of Sciences Beijing 100049China China-Pakistan Joint Research Center on Earth Sciences CAS-HECIslamabad 45320Pakistan The University of Poonch Rawalakot Rawalakot 12350Pakistan

The eastern road section of the ChinaPakistan Economic Corridor(CPEC)traverses the challenging terrain of northern Pakistan,where frequent landslides pose a significant threat to socioeconomic development and infrastructure.However,the insufficient data on landslide hazards presents a substantial challenge to practical mitigation efforts.Therefore,we conducted an extensive study to gain insight into landslide assessment along the Mansehra-Muzaffarabad-Mirpur and Mangla(MMMM)Expressway.This study involved preparing a landslide inventory,analyzing landslide causative factors,and developing landslide susceptibility models(LSMs)using published data,remote sensing interpretations,field excursions and integrated predictive techniques.We first used Pearson's correlation coefficient(PCC),variable importance factors(VIF),and information gain ratio(IGR)to evaluate multicollinearity among the selected landslide causative factors(LCFs).Then,the topographic roughness index(TRI)with VIF>5 and PCC>0.7 was considered a redundant factor and thus removed before the data modeling.Finally,we adopted multiple machine-learning methods to analyze landslide susceptibility.The results indicate that the landslide inventory contains 1,776 events,of which 674 were classified based on geometrical and lithological configurations.The IGR results show that the rainfall,lithology,PGA,drainage density,slope,and distance to fault are the most effective LCFs.The AUC values for random forest(RF)(0.901),extreme gradient boosting(XGBoost)(0.884),and K-nearest neighbor(KNN)(0.872)remained higher than evidential belief function(EBF)(0.833),weight of evidence(WoE)(0.820),and certainty factor(CF)(0.810),respectively.The RF model outperformed all other models in terms of prediction.However,these models are accurate but newer in the area;thus,susceptible zones were verified with comprehensive field investigations.The northern and central regions accounted for the high and very high susceptibility classes in the final landslide susceptibility mapping(LSM)com

关键词： landslides MMMM expressway Machine learning landslide susceptibility Northern Pakistan

Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms

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International Journal of Digital Earth 2023年第1期16卷 3384-3416页

作者： Zhuo Chen Danqing Song College of Civil Engineering Sichuan Agricultural UniversityDujiangyanPeople’s Republic of China Sichuan Higher Education Engineering Research Center for Disaster Prevention and Mitigation of Village Construction Sichuan Agricultural UniversityDujiangyanPeople’s Republic of China School of Civil Engineering and Transportation South China University of TechnologyGuangzhouPeople’s Republic of China State Key Laboratory of Subtropical Building Science South China University of TechnologyGuangzhouPeople’s Republic of China

landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study's main,purposes are to explore the potential applications of convolutional neural networks(CNN)hybrid ensemble metaheuristic optimization algorithms,namely beluga whale optimization(BWO)and coati optimization algorithm(COA),for landslide susceptibility mapping in Sichuan Province(China).For this aim,fourteen landslide conditioning factors were compiled in a spatial database.The effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine model.The receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant models.For the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,respectively.In terms of the validation dataset,the CNN-COA model exhibited a higher AUC value of 0.919,while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805,respectively.The results indicate that the CNN-COA model,followed by the CNN-BWO model,and the CNN model,offers the best overall performance for landslide susceptibility analysis.

关键词： landslide susceptibility convolutional neural network beluga whale optimization coati optimization algorithm hybrid models Sichuan Province

Uncertainties of landslide susceptibility prediction considering different landslide types

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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 performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then 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 landslide.Thirdly,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 landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP 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

Evaluation of deep learning algorithms for landslide susceptibility mapping in an alpine-gorge area:a case study in Jiuzhaigou County

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Journal of Mountain Science 2023年第2期20卷 484-500页

作者： WANG Di YANG Rong-hao WANG Xiao LI Shao-da TAN Jun-xiang ZHANG Shi-qi WEI Shuo-you WU Zhang-ye CHEN Chao YANG Xiao-xia College of Earth Sciences Chengdu University of TechnologyChengdu 610059China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China Land consolidation center of Sichuan Province Department of Natural Resources of Sichuan ProvinceChengdu 610072China

With its high mountains,deep valleys,and complex geological formations,the Jiuzhaigou County has the typical characteristics of a disaster-prone mountainous region in southwestern China.On August 8,2017,a strong Ms 7.0 earthquake occurred in this region,causing some of the mountains in the area to become loose and cracked.Therefore,a survey and evaluation of landslides in this area can help to reveal hazards and take effective measures for subsequent disaster management.However,different evaluation models can yield different spatial distributions of landslide susceptibility,and thus,selecting the appropriate model and performing the optimal combination of parameters is the most effective way to improve susceptibility evaluation.In order to construct an evaluation indicator system suitable for Jiuzhaigou County,we extracted 12 factors affecting the occurrence of landslides,including slope,elevation and slope surface,and made samples.At the core of the transformer model is a self-attentive mechanism that enables any two of the features to be interlinked,after which feature extraction is performed via a forward propagation network(FFN).We exploited its coding structure to transform it into a deep learning model that is more suitable for landslide susceptibility evaluation.The results show that the transformer model has the highest accuracy(86.89%),followed by the random forest and support vector machine models(84.47%and 82.52%,respectively),and the logistic regression model achieves the lowest accuracy(79.61%).Accordingly,this deep learning model provides a new tool to achieve more accurate zonation of landslide susceptibility in Jiuzhaigou County.

关键词： Jiuzhaigou landslide susceptibility Transformer Model Deep learning Forest

An Investigation of landslide susceptibility Using Logistic Regression and Statistical Index Methods in Dailekh District, Nepal

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Chinese Geographical Science 2022年第5期32卷 834-851页

作者： DIL Kumar RAI XIONG Donghong ZHAO Wei ZHAO Dongmei ZHANG Baojun NIRMAL Mani DAHAL WU Yanhong MUHAMMAD Aslam BAIG Institute of Mountain Hazards and Environment(IMHE) Chinese Academy of SciencesChengdu 610041China University of Chinese Academy of Sciences Beijing 100049China Kathmandu Center for Research and Education Chinese Academy of Sciences-Tribhuvan UniversityKathmandu 44613Nepal

landslide distribution and susceptibility mapping are the fundamental steps for landslide-related hazard and disaster risk management activities, especially in the Himalaya region which has resulted in a great deal of death and damage to property. To better understand the landslide condition in the Nepal Himalaya, we carried out an investigation on the landslide distribution and susceptibility using the landslide inventory data and 12 different contributing factors in the Dailekh district, Western Nepal. Based on the evaluation of the frequency distribution of the landslide, the relationship between the landslide and the various contributing factors was determined.Then, the landslide susceptibility was calculated using logistic regression and statistical index methods along with different topographic(slope, aspect, relative relief, plan curvature, altitude, topographic wetness index) and non-topographic factors(distance from river, normalized difference vegetation index(NDVI), distance from road, precipitation, land use and land cover, and geology), and 470(70%) of total 658 landslides. The receiver operating characteristic(ROC) curve analysis using 198(30%) of total landslides showed that the prediction curve rates(area under the curve, AUC) values for two methods(logistic regression and statistical index) were 0.826, and 0.823with success rates of 0.793, and 0.811, respectively. The values of R-Index for the logistic regression and statistical index methods were83.66 and 88.54, respectively, consisting of high susceptible hazard classes. In general, this research concluded that the cohesive and coherent natural interplay of topographic and non-topographic factors strongly affects landslide occurrence, distribution, and susceptibility condition in the Nepal Himalaya region. Furthermore, the reliability of these two methods is verified for landslide susceptibility mapping in Nepal’s central mountain region.

关键词： landslide characteristics landslide susceptibility logistic regression statistical index Nepal Himalaya

Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility

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Journal of Mountain Science 2022年第6期19卷 1670-1698页

作者： Pedro LIMA Stefan STEGER Thomas GLADE Franny G.MURILLO-GARCIA University of Vienna Institute of Geography and Regional ResearchUniversitatsstrafie 7lowViennaAustria Eurac Research Institute for Earth ObservationDrususallee l/Viale Druso 139100Bozen-BolzanoItaly Institute of Geography National Autonomous University of Mexico(UNAM)Circuito ExteriorCiudad Universitaria04510CoyoacanMexico CityMexico

In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of Dd LSM. This research begins with an introduction of the theoretical aspects of Dd LSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent,inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitativeoriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017-2020. The originality of this research is acknowledged by combining together:(a) a recap of important theoretical aspects of Dd LSM;(b) a bibliometric analysis on the topic;(c) a quantitative-oriented review of relevant publications;and(d) a systematic summary of the findings, indicating important aspects and potential developments related to the Dd LSM research topic. The results show that Dd LSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used. Simple classifiers, such as, logistic regression

关键词： Review landslide susceptibility Statistical models Machine learning Bibliometrics

Assessment of landslide susceptibility using DBSCAN-AHD and LD-EV methods

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Journal of Mountain Science 2022年第1期19卷 184-197页

作者： MAO Yi-min MWAKAPESA Deborah S LI Yi-can XU Kai-bin NANEHKARAN Yaser A ZHANG Mao-sheng School of Information Engineering Jiangxi University of Science and TechnologyGanzhou 341000China Key Laboratory for Geo-hazards in Loess Area Xi’an 710054China Northwest Nonferrous Geological Mining Group Limited Company Xi’an 710054China

landslide susceptibility assessment plays a vital role in understanding landslide information in advance and taking preventive as well as control measures. The need to initially specify the number of clusters, difficulty in handling noise and quantifying rainfall data, limits the application of traditional clustering models in landslide susceptibility assessment which then lowers their performance accuracy. Thus, to overcome these limitations, this study proposed an improved clustering algorithm titled the DBSCAN-AHD algorithm, which combines the traditional DBSCAN(Density Based Spatial Clustering of Application with Noise) algorithm and an Adaptive Hausdorff Distance(AHD) for landslide susceptibility modeling. Firstly, AHD was introduced to the traditional DBSCAN to quantify rainfall. Then,the DBSCAN-AHD grouped the mapping units with similar topology and geology characteristics into subclasses without specifying the number of clusters in advance for its ability to handle noise in the data.Furthermore, the LD-EV(landslide density or eigenvalues) approach was introduced to obtain the susceptibility levels using K-means algorithm. Finally,to verify the model’s performance, statistical indices and the area under the curve(AUC) were applied and compared to the traditional DBSCAN, KPSO, Kmeans and Hierarchical Clustering algorithms,whereby, the proposed model outperformed the others. Also, the obtained susceptibility map can provide references in taking relevant preventive and control measures.

关键词： landslide susceptibility DBSCAN Machine learning algorithm Uncertain data Clustering algorithm