Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace-based naive Bayes tree in Zigui County of the Three Gorges Reservoir Area, China

A novel machine learning ensemble model that is a hybridization of Bagging and random subspace-based naive Bayes tree (RSNBtree), named as BRSNBtree, was used to prepare a landslide susceptibility map for Zigui County of the Three Gorges Reservoir Area, China. The proposed method is implemented by using the Bagging scheme to integrate the base-level RSNBtree model. To predict landslide susceptibility for the study area, a spatial database consisted of 807 landslides and 11 conditioning factors has been prepared. Evaluation of conditioning factors was conducted using the Pearson correlation coefficient and Relief-F method. The results indicate that all factors except the topographic wetness index can be accepted as modeling inputs. Particularly, the distance to rivers is the most important factor in landslide susceptibility prediction. The performance of landslide models was evaluated using statistical indices and areas under the receiver operatic characteristic curve (AUC). The support vector machines (SVM) and random forest (RF) were adopted for the comparison with our methods. Results show that the BRSNBtree (AUC = 0.968) achieves the highest prediction performance, which successfully refines the RSNBtree (AUC = 0.938) and outperforms the RF (AUC = 0.949) and SVM (AUC = 0.895). Therefore, the proposed BRSNBtree presents advantages in targeting landslide susceptible areas and provides a promising method for landslide susceptibility assessment. The developed susceptibility maps could facilitate effective landslide risk management for this landslide-prone area.

Automated summary

A novel machine learning ensemble model, BRSNBtree, was proposed to predict landslide susceptibility in Zigui County of the Three Gorges Reservoir Area. The results showed that the distance to rivers was the most important factor in predicting landslide susceptibility, and BRSNBtree outperformed other methods in terms of prediction performance.