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Title
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A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment
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Type
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JournalPaper
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Keywords
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Landslide, evidential belief function, weight of evidence, logistic model tree, China
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Abstract
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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.
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Researchers
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Baharin Ben Ahmad (Not In First Six Researchers), Manna Xi (Not In First Six Researchers), Mingzhe Ma (Not In First Six Researchers), Jiarui Hui (Not In First Six Researchers), Di Pan (Not In First Six Researchers), Wei Li (Not In First Six Researchers), Haoyuan Hong (Not In First Six Researchers), Chen Guo (Fifth Researcher), Tao Li (Fourth Researcher), Ataollah Shirzadi (Third Researcher), Himan Shahabi (Second Researcher), Wei Chen (First Researcher)
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