Universiti Teknologi Malaysia Institutional Repository

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

Chen, Wei and Shahabi, Himan and Shirzadi, Ataollah and Li, Tao and Guo, Chen and Hong, Haoyuan and Li, Wei and Pan, Di and Hui, Jiarui and Ma, Mingzhe and Xi, Manna and Ahmad, Baharin (2018) A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto International, 33 (12). pp. 1398-1420. ISSN 1010-6049

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Official URL: http://dx.doi.org/10.1080/10106049.2018.1425738

Abstract

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.

Item Type:Article
Uncontrolled Keywords:China, evidential belief function, landslide, logistic model tree, weight of evidence
Subjects:N Fine Arts > NA Architecture
Divisions:Geoinformation and Real Estate
ID Code:84526
Deposited By: Yanti Mohd Shah
Deposited On:11 Jan 2020 15:32
Last Modified:11 Jan 2020 15:32

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