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

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College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
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State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, Hubei, China
3
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Authors to whom correspondence should be addressed.
Entropy 2019, 21(2), 106; https://doi.org/10.3390/e21020106
Received: 25 December 2018 / Revised: 15 January 2019 / Accepted: 22 January 2019 / Published: 23 January 2019
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. View Full-Text
Keywords: rotation forest; credal decision tree; ensemble model; machine learning; landslide rotation forest; credal decision tree; ensemble model; machine learning; landslide
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He, Q.; Xu, Z.; Li, S.; Li, R.; Zhang, S.; Wang, N.; Pham, B.T.; Chen, W. Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling. Entropy 2019, 21, 106.

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