A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment

作者：Chen, Wei; Shahabi, Himan^*; Shirzadi, Ataollah; Li, Tao; Guo, Chen; Hong, Haoyuan; Li, Wei; Pan, Di; Hui, Jiarui; Ma, Mingzhe; Xi, Manna; Bin Ahmad, Baharin

来源：Geocarto International, 2018, 33(12): 1398-1420.

DOI：10.1080/10106049.2018.1425738

• Landslide
• evidential belief function
• weight of evidence
• logistic model tree
• China

This 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.

• 出版日期2018-12-2
• 单位西安科技大学; 南京师范大学; 山东科技大学