Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid m...Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.展开更多
The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e.multilayer perception neural nets(MLP),kernel logistic regression(KLR),random forest(RF),and m...The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e.multilayer perception neural nets(MLP),kernel logistic regression(KLR),random forest(RF),and multivariate adaptive regression splines(MARS);novel ensemble approaches i.e.MLP-Bagging,KLR-Bagging,RFBagging and MARS-Bagging in the Kurseong-Himalayan region.For the ensemble models the RF,KLR,MLP and MARS were used as base classifiers,and Bagging was used as meta classifier.Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide.Compiling 303 landslide locations to calibrate and test the models,an inventory map was created.Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility.Applying receiver operating characteristic(ROC),precision,accuracy,incorrectly categorized proportion,mean-absolute-error(MAE),and root-mean-square-error(RMSE),the LSMs were subsequently verified.The different validation results showed RF-Bagging(AUC training 88.69%&testing 92.28%)with ensemble Meta classifier gives better performance than the MLP,KLR,RF,MARS,MLP-Bagging,KLR-Bagging,and MARSBagging based LSMs.RF model showed that the slope,altitude,rainfall,and geomorphology played the most vital role in landslide occurrence comparing the other LCFs.These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.展开更多
Abstract The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The re...Abstract The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. We use a nonparametric approach based on a combination of kernel logistic regression and e-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies.展开更多
基金funded by National Key Research and Development Program of China, Ecological Safety Guarantee Technology and Demonstration Channel and Slope Treatment Project in Loess Hilly and Gully Area (Grant No. 2017YFC0504700)。
文摘Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.
文摘The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e.multilayer perception neural nets(MLP),kernel logistic regression(KLR),random forest(RF),and multivariate adaptive regression splines(MARS);novel ensemble approaches i.e.MLP-Bagging,KLR-Bagging,RFBagging and MARS-Bagging in the Kurseong-Himalayan region.For the ensemble models the RF,KLR,MLP and MARS were used as base classifiers,and Bagging was used as meta classifier.Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide.Compiling 303 landslide locations to calibrate and test the models,an inventory map was created.Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility.Applying receiver operating characteristic(ROC),precision,accuracy,incorrectly categorized proportion,mean-absolute-error(MAE),and root-mean-square-error(RMSE),the LSMs were subsequently verified.The different validation results showed RF-Bagging(AUC training 88.69%&testing 92.28%)with ensemble Meta classifier gives better performance than the MLP,KLR,RF,MARS,MLP-Bagging,KLR-Bagging,and MARSBagging based LSMs.RF model showed that the slope,altitude,rainfall,and geomorphology played the most vital role in landslide occurrence comparing the other LCFs.These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.
文摘Abstract The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. We use a nonparametric approach based on a combination of kernel logistic regression and e-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies.
