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landslide susceptibility mapping of mountain roads based on machine learning combined model

Journal of Mountain Science 2023年第5期20卷 1232-1248页

作者： DOU Hong-qiang HUANG Si-yi JIAN Wen-bin WANG Hao Zijin School of Geology and Mining Fuzhou UniversityFuzhou 350116China Fujian Key Laboratory of Geohazard Prevention Fuzhou 350002China

landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations.Then,a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model.This paper proposes a combined machine-learning model to address the issues.The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes.The mountain roads in the Youxi County,Fujian Province,China were used for the landslide susceptibility mapping.Three most frequently used machine learning techniques,support vector machine(SVM),random forest(RF),and artificial neural network(ANN)models,were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system.The global minimum variance portfolio was utilized to construct a machine learning combined model.5-fold cross-validation,statistical indexes,and AUC(Area Under Curve)values were implemented to evaluate the predictive accuracy of the landslide susceptibility model.The mean AUC values for the SVM,RF,and ANN models in the training stage were 89.2%,88.5%,and 87.9%,respectively,and 78.0%,73.7%,and 76.7%,respectively,in the validating stage.In the training and validation stages,the mean AUC values of the combined model were 92.4% and 87.1%,respectively.The combined model provides greater prediction accuracy and model robustness than one single model.

关键词： landslide susceptibility mapping Factor analysis Machinelearning Combinedmodel Mountain roads

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A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network,random forest,and logistic regression models

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International Journal of Digital Earth 2023年第1期16卷 408-429页

作者： Zheng Zhao Jianhua Chen State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China College of Geophysics Chengdu University of TechnologyChengduPeople’s Republic of China

The selection of discretization criteria and interval numbers of landslide-related environmental factors generally fails to quantitatively determine orfilter,resulting in uncertainties and limitations in the performance of machine learning(ML)methods for landslide susceptibility mapping(LSM).The aim of this study is to propose a robust discretization criterion(RDC)to quantify and explore the uncertainty and subjectivity of different discretization methods.The RDC consists of two steps:raw classification dataset generation and optimal dataset extraction.To evaluate the robustness of the proposed RDC method,Lushan County of Sichuan Province in China was chosen as the study area to generate the LSM based on three datasets(optimal dataset,original dataset with continuous values,and statistical dataset)using three popular ML methods,namely,convolution neural network,random forest,and logistic regression.The results show that the areas under the receiver operating characteristic curve(AUCs)of the optimal dataset for the abovementioned ML models are 0.963,0.961,and 0.930 which are higher than those of the original dataset(0.938,0.947,and 0.900)and statistical dataset(0.948,0.954,and 0.897).In conclusion,the RDC method can extract the more representative features from environmental factors and outperform the other conventional discretization methods.

关键词： Discretization machine learning landslide susceptibility mapping spatial statistics convolution neural network

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landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

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Geoscience Frontiers 2021年第5期12卷 355-373页

作者： Xinzhi Zhou Haijia Wen Yalan Zhang Jiahui Xu Wengang Zhang School of Civil Engineering Chongqing UniversityChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing 400045China Key Laboratory of New Technology for Construction of Cities in Mountain Area Ministry of EducationChongqing UniversityChongqing 400045China Key Laboratory of GIS Application Research Chongqing Normal UniversityChongqing 401331China

The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points,which was randomly divided into two datasets for model training(70%)and model testing(30%).22 factors were initially selected to establish a landslide factor database.We applied the GeoDetector and recursive feature elimination method(RFE)to address factor optimization to reduce information redundancy and collinearity in the data.Thereafter,the frequency ratio method,multicollinearity test,and interactive detector were used to analyze and evaluate the optimized factors.Subsequently,the random forest(RF)model was used to create a landslide susceptibility map with original and optimized factors.The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve(AUC)and accuracy.The accuracy of the two hybrid models(0.868 for GeoDetector-RF and 0.869 for RFE-RF)were higher than that of the RF model(0.860),indicating that the hybrid models with factor optimization have high reliability and predictability.Both RFE-RF GeoDetector-RF had higher AUC values,respectively 0.863 and 0.860,than RF(0.853).These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.

关键词： landslide susceptibility mapping GeoDetector Recursive feature elimination Random forest Factor optimization

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Machine learning solution for regional landslide susceptibility based on fault zone division strategy

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Journal of Mountain Science 2024年第5期21卷 1745-1760页

作者： WANG Yunhao WANG Luqi LIU Songlin SUN Weixin LIU Pengfei ZHU Lin YANG Wenyu GUO Tong School of Civil Engineering Chongqing UniversityChongqing 400045China Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing UniversityMinistry of EducationChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing 400045China Institute of Geological Environment Monitoring Chongqing 401147China Chongqing 205 Geological Team Chongqing 401121China

landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study performed qualitative analysis of fault zones and proposed a zoning method to assess the landslide susceptibility in Chengkou County, Chongqing Municipality, China. The region within a distance of 1 km from the faults was designated as sub-zone A, while the remaining area was labeled as sub-zone B. To accomplish the assessment, a dataset comprising 388 historical landslides and 388 non-landslide points was used to train the random forest model. 10-fold cross-validation was utilized to select the training and testing datasets for the model. The results of the models were analyzed and discussed, with a focus on model performance and prediction uncertainty. By implementing the proposed division strategy based on fault zone, the accuracy, precision, recall, F-score, and AUC of both two sub-zones surpassed those of the whole region. In comparison to the results obtained for the whole region, sub-zone B exhibited an increase in AUC by 6.15%, while sub-zone A demonstrated a corresponding increase of 1.66%. Moreover, the results of 100 random realizations indicated that the division strategy has little effect on the prediction uncertainty. This study introduces a novel approach to enhance the prediction accuracy of the landslide susceptibility mapping model in areas with multiple fault zones.

关键词： landslide susceptibility mapping Fault division strategy Random forest GIS

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GIS-based ordered weighted averaging and Dempster-Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran

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International Journal of Digital Earth 2014年第8期7卷 688-708页

作者： Bakhtiar Feizizadeh Thomas Blaschke Hossein Nazmfar Department of Physical Geography Centre for Remote Sensing and GISUniversity of TabrizTabrizIran Department of Geoinformatics(Z_GIS) University of SalzburgSalzburgAustria Department of Geography and Urban Planning University of Mohaghegh ArdabiliArdabilIran

In this paper,GIS-based ordered weighted averaging(OWA)is applied to landslide susceptibility mapping(LSM)for the Urmia Lake Basin in northwest Iran.Nine landslide causal factors were used,whereby the respective parameters were extracted from an associated spatial database.These factors were evaluated,and then the respective factor weight and class weight were assigned to each of the associated factors using analytic hierarchy process(AHP).A landslide suscept-ibility map was produced based on OWA multicriteria decision analysis.In order to validate the result,the outcome of the OWA method was qualitatively evaluated based on an existing inventory of known landslides.Correspondingly,an uncertainty analysis was carried out using the Dempster-Shafer theory.Based on the results,very strong support was determined for the high susceptibility category of the landslide susceptibility map,while strong support was received for the areas with moderate susceptibility.In this paper,we discuss in which respect these results are useful for an improved understanding of the effectiveness of OWA in LSM,and how the landslide prediction map can be used for spatial planning tasks and for the mitigation of future hazards in the study area.

关键词： GIS-multicriteria decision analysis OWA uncertainty analysis belief landslide susceptibility mapping Urmia Lake Basin

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An Optimized Random Forest Model and Its Generalization Ability in landslide susceptibility mapping:Application in Two Areas of Three Gorges Reservoir,China

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Journal of Earth Science 2020年第6期31卷 1068-1086页

作者： Deliang Sun Jiahui Xu Haijia Wen Yue Wang 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 400045China National Joint Engineering Research Centre of Geohazards Prevention in the Reservoir Areas Chongqing 400044China School of Civil Engineering Chongqing UniversityChongqing 400045China

Numerous researches have been published on the application of landslide susceptibility assessment models;however,they were only applied in the same areas as the models were originated,the effect of applying the models to other areas than the origin of the models has not been explored.This study is purposed to develop an optimized random forest(RF)model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping(LSM),and then explore its generalization ability not only in the area where the model is originated but also in area other than the origin of the model.Two typical counties(Fengjie County and Wushan County)in the Three Gorges Reservoir area,China,which have the same terrain and geological conditions,were selected as an example.To begin with,landslide inventory was prepared based on field investigations,satellite images,and historical records,and 1522 landslides were then identified in Fengjie County.22 landslide-conditioning factors under the influence of topography,geology,environmental conditions,and human activities were prepared.Then,combined with 10-fold cross-validation,three typical ratios of positive-to-negative cells,i.e.,1:1,1:5,and 1:10,were adopted for comparative analyses.An optimized RF model(Fengjie-based model)with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed.Finally,the Fengjie-based model was applied to Fengjie County and Wushan County,and the confusion matrix and area under the receiver operating characteristic(ROC)curve value(AUC)were used to estimate the accuracy.The Fengjie-based model delivered high stability and predictive capability in Fengjie County,indicating a great generalization ability of the model to the area where the model is originated.The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value,indicating the Fengjiebased model had a great generalization ability in area other than the origin of the model.The Fengjiebased model in this study

关键词： landslide susceptibility mapping generalization ability random forest Three Gorges Reservoir area 10-fold cross-validation

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Ensemble learning framework for landslide susceptibility mapping:Different basic classifier and ensemble strategy

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Geoscience Frontiers 2023年第6期14卷 170-190页

作者： Taorui Zeng Liyang Wu Dario Peduto Thomas Glade Yuichi S.Hayakawa Kunlong Yin Institute of Geological Survey China University of GeosciencesWuhan 430074China ENGAGE—Geomorphic Systems and Risk Research Department of Geography and Regional ResearchUniversity of Vienna1010 ViennaAustria Faculty of Engineering China University of GeosciencesWuhanChina Department of Civil Engineering University of Salerno84084 SalernoFiscianoItaly Faculty of Environmental Earth Science Hokkaido UniversityHokkaido 060-0810Japan

The application of ensemble learning models has been continuously improved in recent landslide susceptibility research,but most studies have no unified ensemble framework.Moreover,few papers have discussed the applicability of the ensemble learning model in landslide susceptibility mapping at the township level.This study aims at defining a robust ensemble framework that can become the benchmark method for future research dealing with the comparison of different ensemble models.For this purpose,the present work focuses on three different basic classifiers:decision tree(DT),support vector machine(SVM),and multi-layer perceptron neural network model(MLPNN)and two homogeneous ensemble models such as random forest(RF)and extreme gradient boosting(XGBoost).The hierarchical construction of deep ensemble relied on two leading ensemble technologies(i.e.,homogeneous/heterogeneous model ensemble and bagging,boosting,stacking ensemble strategy)to provide a more accurate and effective spatial probability of landslide occurrence.The selected study area is Dazhou town,located in the Jurassic red-strata area in the Three Gorges Reservoir Area of China,which is a strategic economic area currently characterized by widespread landslide risk.Based on a long-term field investigation,the inventory counting thirty-three slow-moving landslide polygons was drawn.The results show that the ensemble models do not necessarily perform better;for instance,the Bagging based DT-SVM-MLPNNXGBoost model performed worse than the single XGBoost model.Amongst the eleven tested models,the Stacking based RF-XGBoost model,which is a homogeneous model based on bagging,boosting,and stacking ensemble,showed the highest capability of predicting the landslide-affected areas.Besides,the factor behaviors of DT,SVM,MLPNN,RF and XGBoost models reflected the characteristics of slow-moving landslides in the Three Gorges reservoir area,wherein unfavorable lithological conditions and intense human engineering activities(i.e.,reservoir water level fluctuation,residen

关键词： Three Gorges Reservoir Area landslide susceptibility mapping Ensemble learning framework Uncertainty research

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A new method of pseudo absence data generation in landslide susceptibility mapping with a case study of Shenzhen

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Science China(Technological Sciences) 2010年第S1期53卷 75-84页

作者： XIAO ChenChao TIAN Yuan SHI WenZhong GUO QingHua WU Lun Institute of Remote Sensing and Geographic Information System Peking UniversityBeijing 100871China University of Salzburg Z_GISCenter for GeoinformaticsSchillerstraβe 305020 SalzburgAustria Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong KongChina School of Engineering University of California MercedMerced 95343USA

landslide susceptibility mapping is a typical two-class classification problem where generating pseudo absence (non-slide) data plays an important role.In this paper,a new method,target space exteriorization sampling method (TSES),is presented to generate pseudo absence data based on presence data directly in feature space.TSES exteriorizes a presence sample to become a pseudo absence one by replacing the value of one of its features with a new one outside the value range of this feature of all presence data.This method is compared with two existing methods,buffer controlled sampling (BCS) and iteratively refined sampling (IRS),in a study area of Shenzhen city.The pseudo absence data generated by each of these three methods are organized into 20 subsets with increasing data sizes to study the effects of the proportion of pseudo absence data to presence data.The landslide susceptibility maps of the study area are calculated with all these datasets by general additive model (GAM).It can be concluded that,through a 10-fold validation,TSES and IRS-based models have similar AUC values that are both greater than that of BCS,but TSES outperforms BCS and IRS in prediction efficiency.TSES results also have more reasonable spatial and histogram distributions than BCS and IRS,which can support categorization of an area into more susceptibility ranks,while IRS shows a tendency to separate the whole study area into two susceptibility extremes.It can be also concluded that when using BCS,the pseudo absence data proportion to the presence data would be about 50% to get a considerable result,while for IRS or TSES the minimum proportion is 40%.

关键词： pseudo absence data landslide susceptibility mapping GIS GAM

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GIS and ANN model for landslide susceptibility mapping

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Journal of Geographical Sciences 2001年第3期11卷 374-381页

作者： XU Zeng-wang (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China) State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources Research CAS

landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage Une are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.

关键词： GIS artificial neural network model landslide susceptibility mapping

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landslide susceptibility mapping based on Support Vector Machine:A case study on natural slopes of Hong Kong,China

Landslide susceptibility mapping based on Support Vector Mac...

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中国科学院地质与地球物理研究所2008学术年会

作者： L.G.Tham Department of Civil Engineering The University of Hong Kong

The Support Vector Machine(SVM) is an increasingly popular learning procedure based on statistical learning theory,and involves a training phase in which the model is trained by a training dataset of associated input and target output values.The trained model is then used to evaluate a separate set of testing data.There are two main ideas underlying the SVM for discriminant-type problems.The first is an optimum linear separating hyperplane that separates the data patterns.The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space.In this paper,an overview of the SVM,both one-class and two-class SVM methods,is first presented followed by its use in landslide susceptibility mapping.A study area was selected from the natural terrain of Hong Kong,and slope angle,slope aspect,elevation,profile curvature of slope,lithology,vegetation cover and topographic wetness index(TWI) were used as environmental parameters which influence the occurrence of landslides.One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively.The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression(LR) method.It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM.However,one-class SVM,which only requires failed cases,has an advantage over the other two methods as only"failed"case information is usually available in landslide susceptibility mapping.

关键词： landslide susceptibility mapping Support Vector Machine（SVM） One-class sample Two-class sample Logistic regression method Hong Kong

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