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51.
In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models. 相似文献
52.
Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management 总被引：1，自引：0，他引：1
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 zonation.This 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 subse-quently 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 model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter 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 fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,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 area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices. 相似文献
53.
Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer 总被引：1，自引：0，他引：1
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 areas.In 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 mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,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,respectively.In 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,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,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 considered due to its excellent accuracy and efficiency. 相似文献
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采用工程地质钻探、物探、地质测绘及室内试验等技术方法探讨飞鹅山Ⅲ号滑坡形成机理与防治技术。结果表明：1）滑坡体主要岩性为泥质粉砂岩,飞鹅山滑坡属于新形成的深层中型牵引式滑坡,在平面上呈圈椅状。2）滑坡属于双层滑面滑坡,主滑面以中型深层滑坡为主,主滑体上部发育中型中厚层滑坡。3）滑坡产生的原因为：(1)泥质粉砂岩倾向与坡向基本一致,且岩层倾角为中等倾角;(2)人工开挖使坡脚形成高陡临空面,抗滑力大为降低;(3)雨水沿层面及节理裂隙入渗至坡体深部,大大增加岩土体容重,同时泥质粉砂岩遇水软化,抗剪强度显著降低。4）结合该滑坡区地质环境条件,采用坡面削坡+锚杆（索）+格构梁+双排预应力锚拉抗滑桩+三维网植草绿化+截排水+毛石挡墙的综合治理方法进行防治,监测结果显示该滑坡变形及位移已得到有效控制,整治效果良好。 相似文献
57.
1996年10月28日发生在金沙江下虎跳峡口的滑石板滑坡,因堵塞金沙江、摧毁公路而引起高度重视。野外调查发现,滑石板滑坡是典型结构面控制的顺层基岩滑坡,层面和两组近于垂直层面的节理将边坡厚层灰岩切割成不同规模的菱形块体,长期的风化作用、重力、降雨、人工开挖公路等因素作用下,这些菱形块体从顺层稳定状态演化为稳定性较差的悬挂体,受河谷边坡斜向割切影响,表现为梯级叠次悬挂,最外边的悬挂体稳定性最差,最容易发生快速滑动。因此,该滑坡每次发生破坏滑动的体积不会很大,但是具有多次重复发生的特点,值得高度注意。 相似文献
58.
喜马拉雅山东南地区地质灾害发育规律初步研究 总被引：2，自引：0，他引：2
利用遥感手段,结合MapGis,研究了喜马拉雅山东南地区地质灾害的发育情况,发现本区发育的主要地质灾害有滑坡、崩塌、泥石流、冰湖以及堰塞湖。其中崩塌、滑坡、泥石流斜坡地质灾害是本区最重要的地质灾害类型,占到总灾害数量的95.3%。在此基础上对喜马拉雅山东南地区地质灾害发育规律初步研究,发现本区地质灾害的发育在空间上的分布并非均匀,而是具有丛集性的特点。滑坡灾害主要发育在隆子和朗县。泥石流灾害比较严重的有米林、隆子和洛扎3县,而崩塌则主要集中在隆子县。研究发现,本区滑坡发育与地层、地形坡度以及土地类型关系密切,其中修康群、日当组和念青唐古拉群是本区的易滑地层。涅如组由于面积大,其中发育的滑坡较多,但是滑坡的发育率只略高于本区的平均水平。统计表明,16～30的坡度范围是滑坡最容易发生的。大于45以上的坡段很少发生滑坡。灌木林和天然草地这两种土地类型滑坡发育率最高。对于泥石流,研究表明,涅如组中泥石流发育面积最大,发育率也最高。泥石流发育的最适宜坡度也是16～30这样一个坡度范围。冰川和永久积雪区则最易发生泥石流。崩塌发育与地层类型、坡度的关系较为密切,崩塌主要发育在涅如组中,并且集中在坡度大于60以上的陡坡段中。这些初步成果的取得,是以后进行该区地质灾害空间预测的基础。 相似文献
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