A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment |
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| Authors: | Wei Chen Ataollah Shirzadi Tao Li Chen Guo Haoyuan Hong |
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| Institution: | 1. College of Geology &2. Environment, Xi’an University of Science and Technology, Xi’an, China;3. Shandong Provincial Key Laboratory of Depositional Mineralization &4. Sedimentary Minerals, Shandong University of Science and Technology, Qingdao, China;5. Faculty of Natural Resources, Department of Rangeland and Watershed Management, University of Kurdistan, Sanandaj, Iran;6. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China;7. State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, China;8. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, China |
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| Abstract: | AbstractThis study addresses landslide susceptibility mapping (LSM) using a novel ensemble approach of using a bivariate statistical method (weights of evidence WoE] and evidential belief function EBF])-based logistic model tree (LMT) classifier. The performance and prediction capability of the ensemble models were assessed using the area under the ROC curve (AUROC), standard error, 95% confidence intervals and significance level P. Model performance analyses indicated that the AUROC values of the WoE–LMT ensemble model using the training and validation data-sets were 86.02 and 85.9%, respectively, whereas those of the EBF–LMT ensemble model were 88.2 and 87.8%, respectively. On the other hand, the AUC curves for the four landslide susceptibility maps indicated that the AUC values of the ensemble models of WoE–LMT (85.11 and 83.98%) and EBF–LMT (86.21 and 85.23%) could improve the performance and prediction accuracy of single WoE (84.23 and 82.46%) and EBF (85.39 and 81.33%) models for the training and validation data-sets. |
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| Keywords: | Landslide evidential belief function weight of evidence logistic model tree China |
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