Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Issue 3 (1st February 2022)

• Record Type: Journal Article
• Authors: Pham, Binh Thai; Phong, Tran Van; Nguyen-Thoi, Trung; Parial, Kajori; K. Singh, Sushant; Ly, Hai-Bang; Nguyen, Kien Trung; Ho, Lanh Si; Le, Hiep Van; Prakash, Indra
• Is Part Of: Geocarto international. Volume 37:Issue 3(2022)
• Page Start: 735
• Page End: 757
• Publication Date: 2022-02-01
• Subjects: Hybrid modeling -- machine learning -- ensemble learning -- decision trees -- Gis -- Vietnam; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2020.1737972
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: In this study, we have developed five spatially explicit ensemble predictive machine learning models for the landslide susceptibility mapping of the Van Chan district of the Yen Bai Province, Vietnam. In the model studies, Random Subspace (RSS) was used as the ensemble learner with Best First Decision Tree (BFT), Functional Tree (FT), J48 Decision Tree (J48DT), Naïve Bayes Tree (NBT) and Reduced Error Pruning Trees (REPT) as the base classifiers. Data of 167 past and present landslides and various landslide conditioning factors were used for generation of the datasets. The results showed that the RSSFT model achieved the highest performance in terms of Fgiurepredicting future landslides, followed by RSSREPT, RSSBFT, RSSJ48, and RSSNBT, respectively. Therefore, the RSSFT model was found to be more robust model than the other studied models, which can be used in other areas of landslide susceptibility mapping for proper landuse planning and management.