Journal
ADVANCES IN SPACE RESEARCH
Volume 66, Issue 6, Pages 1303-1320Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.asr.2020.05.016
Keywords
Landslide; Machine learning; Ensembles learning; Hybrid modeling; Vietnam
Categories
Funding
- project geological hazards assessment of Dien Bien-Lai Chau fault zone base on application machine learning, artificial intelligence [VAST05.05/20-21]
- senior research assistant program (VAST)
- national project Pliocene-present tectonics in Vietnam islands and continental shelf for assessing geological hazards [KC.09.22/16-20]
- young research assistant program (VAST)
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Landslide 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. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.
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