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
Weathered rockfill materials, characterized by a mixture of soil matrix and rock aggregates, are widely distributed in mountainous areas. These soils are frequently used for subgrade or riprap in engineering practice, and the mobilized shear strength is crucial for analyzing the displacement and stability of these geo-structures. A series of direct shear tests are performed on a gap-graded soil with a full range of coarse fraction. The behavior of gap-graded soils is analyzed, and a simple model is proposed for the evolution of mobilized stress ratio during direct shearing process based on mixture theory. The change of inter-aggregate configuration is incorporated by introducing a structure variable which increases with coarse fraction and decreases approximately linearly with the overall horizontal shear strain in double logarithmic plot. It reasonably reflects a gradually transformation from a matrix-sustained structure into an aggregate-sustained one with the increase of coarse fraction. The model has four parameters, and at least two direct shear tests need to be done for the calibration. Validation of the model is done by using the test data in this work and those from the literature.
Challenged by rapidly changing climate in combination with an increase in anthropogenic pressures, karst groundwater resources in the Old Town of Lijiang (OTLJ), SW China, are diminishing. Higher frequency and longer duration of dried-up periods have been observed at the Heilongtan Park (HP) Springs in recent years. Thus, there is an urgent need for an artificial recharge scheme, aimed at replenishing groundwater in the aquifer and increasing the outflow of the springs to ensure effective water resources management. Evaluation of the scheme feasibility, prior to its implementation, is important. In this study, tracer tests were conducted between the recharge area and receiving springs in order to gain insight into the transport mechanisms of karst groundwater and the structural characteristics of the aquifer. Multiple underground flow paths, exhibiting high conductivity between the recharge area and HP Springs, were revealed by the interpretation of tracer breakthrough curves. Three springs considered as the leakages of the scheme were identified. Moreover, the outflow of springs at HP and OTLJ were predicted to be increased by the artificially recharged water after 9.2 and 12.5 days, respectively. Quantitative analysis of tracer recoveries demonstrates that the springs to be recharged and the springs considered as leakages, respectively, share 45 and 55% of the increased outflow. The feasibility of the scheme has been confirmed by the tracer tests. This report provides references for the evaluation of artificial groundwater recharge and protection strategies, particularly in large and poorly investigated karst spring fields.
