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landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

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Journal of Mountain Science 2017年第2期14卷 249-268页

作者： DU Guo-liang ZHANG Yong-shuang IQBAL Javed YANG Zhi-hua YAO Xin Key Laboratory of Neotectonic Movement and Geohazard Institute of GeomechanicsChinese Academg of Geological SciencesBeijing 100081China Department of Earth Sciences Abbottabad University of Science and TechnologyAbbottabad 22010Pakistan

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

关键词： landslide susceptibility Integrated model Information value method Logistic regression Bailongjiang watershed

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landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

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Geoscience Frontiers 2021年第6期12卷 243-261页

作者： Zizheng Guo Yu Shi Faming Huang Xuanmei Fan Jinsong Huang Faculty of Engineering China University of GeosciencesWuhan 430074China School of Civil Engineering and Architecture of Engineering Nanchang UniversityNanchang 330031China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China ARC Centre of Excellence for Geotechnical Science and Engineering University of NewcastleNSW 2287Australia

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility *** study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree *** zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total *** results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

关键词： landslide susceptibility Frequency ratio C5.0 decision tree K-means cluster Classification Risk management

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landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

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Geoscience Frontiers 2021年第1期12卷 93-107页

作者： Wei Chen Xi Chen Jianbing Peng Mahdi Panahi Saro Lee College of Geology and Environment Xi’an University of Science and TechnologyXi’an 710054China Key Laboratory of Coal Resources Exploration and Comprehensive Utilization Ministry of Natural ResourcesXi’an 710021China Department of Geological Engineering Chang’an UniversityXi’an 710054China Division of Science Education College of Education#4-301Gangwondaehak-gil Chuncheon-siKangwon National UniversityGangwon-do 24341South Korea Geoscience Platform Research Division Korea Institute of Geoscience and Mineral Resources(KIGAM)124Gwahak-ro Yuseong-guDaejeon 34132South Korea Department of Geophysical Exploration Korea University of Science and Technology217 Gajeong-ro Yuseong-guDaejeon 34113South Korea

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous *** this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility *** the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)***,a total of fifteen landslide influencing factors were *** relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)***,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,*** terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,***,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization ***,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO ***,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area

关键词： landslide susceptibility Step-wise weight assessment ratio analysis Adaptive neuro-fuzzy fuzzy inference system Teaching-learning-based optimization Satin bowerbird optimizer

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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 *** 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 *** landslide influencing factors were considered in this *** 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 *** metrics,including the mean probability,model uncertainty,and model prediction skills,were used to evaluate the quality of the susceptibility *** 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 *** AUC(area under curve)values,ranging from 0.8 to 0.86,show that the prediction ability of two mapping units is roughly the *** comparing with the RU,the use of SU can lower the model uncertainties caused by the variation of training *** converted the RU-based assessment results into SU-based *** 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 *** 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

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landslide susceptibility Mapping and Evaluation along a River Valley in China

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Acta Geologica Sinica(English Edition) 2012年第4期86卷 1022-1030页

作者： LI Yanrong Adnan AYDIN XIANG Xiqiong JU Nengpan ZHAO Jianjun Ahmet OZBEK The University of Hong Kong.Hong Kong China The University of Mississippi OxfordUSA Key Laboratory of Karst Environment and Geohazard Prevention(Guizhou University) State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology) The University of Kahramanmaras Sutcu Imam K.MarasTurkey

landslide susceptibility evaluation at regional scale is commonly performed based dominantly on the analysis of geological and geomorphological conditions of historical landslide cases. The main content of this type of evaluation covers identifying key casual factors, their critical groupings and relative importance. The present study demonstrates an application of the above concept to a 90 km long segment of Jinsbajiang River valley in China. Correlations of landslide occurrence with potential causative factors are derived according to interpretation of field investigation. Lithology, orientation of bedding planes, slope angle, stream action, rainfall and earthquake intensity are selectively recognized as identifiable/measurable causative factors to establish a factor domain. The membership grades, for field values of quantitative factors, to the susceptibility classes are determined based on the construction of fuzzy sets, while those for descriptive factors are assigned from a fuzzy score table. The analytic hierarchy process （AHP） is adopted for assigning weights to each individual factor. Subsequently, the evaluation is implemented in a GIS program IDRISI, where four classes of landslide susceptibility are identified and delineated in the subject area. The approach described in the present paper showed consistence with the nature and availability of data for evaluating landslide susceptibility at regional scale. The methodology presented can be effectively employed by relevant authorities to identify risky areas for dislocating major infrastructural project, and develop management strategies for land use.

关键词： Analytic Hierarchy Process (AHP) Fuzzy Set Geographic Information System landslide susceptibility

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landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

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Geoscience Frontiers 2021年第2期12卷 639-655页

作者： Ahmed Mohamed Youssef Hamid Reza Pourghasemi Geology Department Faculty of ScienceSohag UniversityEgypt Geological Hazards Department Applied Geology SectorSaudi Geological SurveyP.O.Box 54141Jeddah21514Saudi Arabia Department of Natural Resources and Environmental Engineering College of AgricultureShiraz UniversityShirazIran

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their *** machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important *** study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi ***,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data *** the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating *** landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and *** area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs *** results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for *** findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other *** outcome of this study and the landslide susceptibility maps would be useful for environmental protection.

关键词： landslide susceptibility Machine learning algorithms Variables importance Saudi Arabia

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landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 3221-3232页

作者： Deliang Sun Jing Wang Haijia Wen YueKai Ding Changlin Mi Key Laboratory of GIS Application Research Chongqing Normal UniversityChongqing 401331China Key Laboratory of New Technology for Construction of Cities in Mountain Area Ministry of EducationChongqing UniversityChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing 400045China School of Civil Engineering Chongqing UniversityChongqing 400045China Natural Resources Development Service Center of Linyi Linyi 276000China

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)***,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM *** investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average *** XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM ***,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM *** boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer ***,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer *** HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test *** model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study *** study offers a scientific reference for LSM and disaster prevention *** study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou *** proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM ***,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.

关键词： landslide susceptibility Hyperparameter optimization Boosting algorithms SHapley additive exPlanations(SHAP)

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Physics-informed optimization for a data-driven approach in landslide susceptibility evaluation

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 3192-3205页

作者： Songlin Liu Luqi Wang Wengang Zhang Weixin Sun Yunhao Wang Jianping Liu School of Civil Engineering Chongqing UniversityChongqing400045China Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing UniversityMinistry of EducationChongqing400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing400045China Chongqing Field Scientific Observation Station for landslide Hazards in Three Gorges Reservoir Area Chongqing UniversityChongqing400045China Chongqing Mingfeng Construction Engineering Co. LtdChongqing405800China

landslide susceptibility mapping is an integral part of geological hazard ***,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear ***,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,*** non-landslide *** this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir *** depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior *** Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility *** proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark ***,the landslide susceptibility map generated by the proposed model demonstrated superior *** result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.

关键词： Physics-informed Machine learning Bedrock depth Scoops 3D landslide susceptibility

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GIS based landslide susceptibility Mapping of Tevankarai Ar Sub-watershed,Kodaikkanal,India using Binary Logistic Regression Analysis

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Journal of Mountain Science 2011年第4期8卷 505-517页

作者： Sujatha E RAMANI Kumarvel PITCHAIMANI Victor Rajamanickam GNANAMANICKAM School of Civil Engineering SASTRA University Indian Institute of Astrophysics Sairam Group of Institutions West Tambaram ChennaiTamilnaduIndia

landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an *** attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression *** Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide *** model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression *** inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent ***,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent *** constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy *** model shows that the most significant parameter contributing to landslides is *** other significant parameters are profile curvature,soil,road,wetness index and *** predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.

关键词： landslide susceptibility Binary Logistic Regression GIS Kodaikkanal India

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Incorporating mitigation strategies in machine learning for landslide susceptibility prediction

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Geoscience Frontiers 2024年第5期15卷 399-414页

作者： Hai-Min Lyu Zhen-Yu Yin Pierre-Yves Hicher Farid Laouafa Key Laboratory for Resilient Infrastructures of Coastal Cities(MOE) College of Civil and Transportation EngineeringShenzhen UniversityShenzhenChina Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Research Institute of Civil Engineering and Mechanics(GeM) UMR CNRS 6183Ecole Centrale de NantesFrance National Institute for Industrial Environment and Risks(INERIS) Verneuil-en-HalatteFrance

This study proposes an approach that considers mitigation strategies in predicting landslide susceptibility through machine learning(ML)and geographic information system(GIS)*** models,such as random forest(RF),logistic regression(LR),and support vector classification(SVC)are incorporated into GIS to predict landslide susceptibilities in Hong *** consider the effect of mitigation strategies on landslide susceptibility,non-landslide samples were produced in the upgraded area and added to randomly created samples to serve as ML models in training *** scenarios were created to compare and demonstrate the efficiency of the proposed approach;Scenario I does not considering landslide control while Scenario II considers mitigation strategies for landslide *** largest landslide susceptibilities are 0.967(from RF),followed by 0.936(from LR)and 0.902(from SVC)in Scenario II;in Scenario I,they are 0.986(from RF),0.955(from LR)and 0.947(from SVC).This proves that the ML models considering mitigation strategies can decrease the current landslide *** comparison between the different ML models shows that RF performed better than LR and SVC,and provides the best prediction of the spatial distribution of landslide susceptibilities.

关键词： Machine learning landslide susceptibility Spatial prediction Mitigation strategies

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