西北地质2024，Vol.57Issue(6)：255-267,13.DOI:10.12401/j.nwg.2023196
基于分形维数耦合支持向量机和熵权模型的滑坡易发性研究
Study of Landslide Susceptibility Mapping Based on Fractal Dimension Integrating Support Vector Machine with Index of Entropy Model
摘要
Abstract
Landslides occur frequently on the Loess Plateau in the north of Baoji City,Shaanxi Province,which seriously threaten the economic development,production and life of the local people.Based on fractal dimen-sion,entropy weight model(IOE),support vector machine model(SVM)and two hybrid models,namely F-IOE and F-SVM,are used to quantitatively predict the possible occurrence range of landslide.First of all,179 land-slide samples were used to make landslide cataloguing maps,70%(125)of the landslide samples were used for training,and the remaining 30%(54)were used for testing.Then,12 kinds of landslide influence factors are ex-tracted,information gain rate and fractal dimension of each factor are calculated respectively,and four landslide vulnerability zoning models are established using training data.Finally,the performance of the model was test-ed using the receiver operating characteristic curve(ROC)and statistical indicators including positive predictive rate(PPR),negative predictive rate(NPR)and accuracy rate(ACC),and the generalization of the model was compared.The results show that F-SVM model has the highest PPR,NPR,ACC and AUC values in training and test data sets respectively,followed by F-IOE model.Finally,F-SVM model is the best among all models.Therefore,the hybrid model based on fractal dimension has more advantages than the original model,which can provide reference for local landslide control decisions.关键词
GIS/滑坡易发性研究/混合模型/分形维数Key words
GIS/research on landslide susceptibility/mixed model/fractal dimension分类
天文与地球科学引用本文复制引用
付泉,党光普,李致博,田润青,石琳,赵鑫,王昆,石磊,吕娜娜..基于分形维数耦合支持向量机和熵权模型的滑坡易发性研究[J].西北地质,2024,57(6):255-267,13.基金项目
陕西省重点研发计划项目(2024SF-YBXM-565),陕西地建土地勘测规划设计院2024年度内部科研项目(KCNY2024-2,KCNY2024-4),陕西省土地工程建设集团内部科研项目(DJNY-ZD-2023-1,DJNY-YB-2023-18,DJNY2024-18)和陕西地建—西安交大土地工程与人居环境技术创新中心开放基金(2024WHZ0240)联合资助. （2024SF-YBXM-565）
