Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

Clicks: 256
ID: 260713
2019
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Abstract
Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.
Reference Key
he2019entropynovel Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Qingfeng He;Zhihao Xu;Shaojun Li;Renwei Li;Shuai Zhang;Nianqin Wang;Binh Thai Pham;Wei Chen;He, Qingfeng;Xu, Zhihao;Li, Shaojun;Li, Renwei;Zhang, Shuai;Wang, Nianqin;Pham, Binh Thai;Chen, Wei;
Journal entropy
Year 2019
DOI
10.3390/e21020106
URL
https://dx.doi.org/10.3390/e21020106
Keywords
Machine learning ensemble model rotation forest landslide credal decision tree

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