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

• Published in: The Science of the total environment 2020-05, Vol.718, p.137231-137231, Article 137231
• Main Authors: Hong, Haoyuan, Liu, Junzhi, Zhu, A-Xing
• Format: Article
• Language: English
• Subjects: Bagging; Forest by penalizing attributes; Integration model; Landslide; LogitBoost alternating decision trees
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2020.137231

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. [Display omitted] •Landslide susceptibility assessment by novel integration models•Support Vector Machine classifier method were used to optimize landslide conditioning factors.•The LADT