Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble

Hong, Haoyuan1,2,3,4; Liu, Junzhi1,2,3; Zhu, A-Xing1,2,3,5,6,7

2020

10.1016/j.scitotenv.2020.137231

Science of the Total Environment   影响因子和分区

18791026

1879-1026

The major target of this study is to design two novel hybrid integration artificial intelligent models, which are denoted as LADT-Bagging and FPA-Bagging, for modeling landslide susceptibility in the Youfanggou district (China). First of all, we prepared a geospatial database in the study area, including 79 landslide points that were divided into a training and validating dataset and 14 landslide conditioning factors. Second, the Support Vector Machines classifier (SVMC) approach was adapted to analyze the predictive capability of the landslide predisposing factors in each method. Then, a multicollinearity analysis using TOL and VIF parameters and Pearson's correlation coefficient methods were applied to verify the multicollinearity and correlation between these factors. Third, the LADT-Bagging and FPA-Bagging models were built by the integration of the LogitBoost alternating decision trees (LADT) with the Bagging ensemble and Forest by Penalizing Attributes (FPA) with the Bagging ensemble, respectively. Besides, heuristic tests were also applied to identify the appropriate values of each model's parameters in order to obtain the best programmer. Finally, for the training dataset, the results reveal that the LADT-Bagging model acquire the largest AUC value (0.980), smallest standard error (SE) (0.0134), narrowest 95% confidence interval (CI) (0.920–0.999), highest accuracy value (AV) (91.03%), highest specificity (94.44%), highest sensitivity (88.10%), highest F-measure (0.9115), lowest MAE (0.2016), lowest RMSE (0.2653), and highest Kappa (0.8205). About the result of validating dataset, it reveal that the LADT-Bagging model acquire the largest AUC value (0.781), the smallest SE (0.0539), the narrowest 95% CI (0.673–0.867), highest AV (71.19%), highest specificity (74.29%), highest sensitivity (69.77%), highest F-measure (0.7195), lowest MAE (0.3509), lowest RMSE (0.4335), and highest Kappa (0.4359). The results indicate that the LADT-Bagging model outperforms the FPA-Bagging, LADT and FPA models. Furthermore, the results of a Wilcoxon signed-rank test demonstrate that LADT-Bagging is significantly statistically different from other models. Therefore, in this study, the proposed new models are useful tools for land use planners or governments in high landslide risk areas.
© 2020 Elsevier B.V.

Landslide LogitBoost alternating decision trees Forest by penalizing attributes Bagging Integration model

EI ; SCI

英语

其他

China Scholarship Council[] ; National Natural Science Foundation of China[41871300] ; [201906860029]

Environmental Sciences & Ecology

Environmental Sciences

WOS:000526029000088

Elsevier B.V.

20200808202683

Adaptive boosting ; Correlation methods ; Decision trees ; Heuristic programming ; Integration ; Land use ; Landslides ; Regression analysis ; Support vector machines

Urban and Regional Planning and Development:403 ; Computer Software, Data Handling and Applications:723 ; Computer Programming:723.1 ; Calculus:921.2 ; Mathematical Statistics:922.2 ; Systems Science:961

ENVIRONMENT/ECOLOGY

EV Compendex

1.Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing; 210023, China
2.State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing; 210023, China
3.Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing; Jiangsu; 210023, China
4.Department of Geography and Regional Research, University of Vienna, Vienna; 1010, Austria
5.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China
6.Department of Geography, University of Wisconsin-Madison, Madison; WI; 53706, United States
7.Center for Social Sciences, Southern University of Science and Technology, Shenzhen, Guangzhou; 518055, China

Hong, Haoyuan,Liu, Junzhi,Zhu, A-Xing. Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble[J]. Science of the Total Environment,2020,718.

Hong, Haoyuan,Liu, Junzhi,&Zhu, A-Xing.(2020).Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble.Science of the Total Environment,718.

Hong, Haoyuan,et al."Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble".Science of the Total Environment 718(2020).