GIS-based ensemble soft computing models for landslide susceptibility mapping
2020; Elsevier BV; Volume: 66; Issue: 6 Linguagem: Inglês
10.1016/j.asr.2020.05.016
ISSN1879-1948
AutoresBinh Thai Pham, Tran Van Phong, T. Nguyen‐Thoi, Phan Trọng Trịnh, Quoc Cuong Tran, Lanh Si Ho, Sushant K. Singh, Tran Thi Thanh Duyen, Nguyễn Thị Tố Loan, Huy Lê, Hiep Van Le, Nguyen Thi Bích Hanh, Nguyen Kim Quoc, Indra Prakash,
Tópico(s)Cryospheric studies and observations
ResumoLandslide susceptibility mapping has become one of the most important tools for the management of landslide hazards. In this study, we proposed a novel approach to improve the performance of Credal Decision Tree (CDT) by using four ensemble frameworks: Bagging, Dagging, Decorate, and Rotation Forest (RF) for landslide susceptibility mapping. A total number of 180 past and present landslides data of the Muong Lay district (Viet Nam) was analyzed and used for generating training and validation of the models. Several standard statistical performance evaluation metrics, such as negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, Kappa, Area Under the receiver operating Characteristic curve (AUC) were used to evaluate performance of the models. Results indicated that all the developed and applied models performed well (AUC: 0.842–0.886) but performance of the RF-CDT (AUC: 0.886) model is the best. Therefore, the RF-CDT ensemble model can be used for the correct landslide susceptibility mapping and for proper landslide management not only of the study area but also other hilly areas of the world.
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