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Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir,China

《中国地球化学学报:英文版》2019年 第5期 | Ting Xiao Kunlong Yin Tianlu Yao Shuhao Liu   Faculty of Engineering China University of Geosciences Wuhan 430074 Hubei China School of Engineering and Technology China University of Geosciences Beijing China
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摘 要:Landslide susceptibility mapping is vital for landslide risk management and urban planning.In this study,we used three statistical models[frequency ratio,certainty factor and index of entropy(IOE)]and a machine learning model[random forest(RF)]for landslide susceptibility mapping in Wanzhou County,China.First,a landslide inventory map was prepared using earlier geotechnical investigation reports,aerial images,and field surveys.Then,the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis.To determine the most effective causal factors,landslide susceptibility evaluations were performed based on four cases with different combinations of factors("cases").In the analysis,465(70%)landslide locations were randomly selected for model training,and 200(30%)landslide locations were selected for verification.The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model.Finally,the receiver operating characteristic(ROC)curve was used to verify the accuracy of each model's results for its respective optimal case.The ROC curve analysis showed that the machine learning model performed better than the other three models,and among the three statistical models,the IOE model with weight coefficients was superior.
【分 类】【天文学、地球科学】
【关键词】 LANDSLIDE SUSCEPTIBILITY mapping STATISTICAL MODEL Machine learning MODEL Four cases
【出 处】 《中国地球化学学报:英文版》2019年 第5期 654-669页 共16页
【收 录】 中文科技期刊数据库

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