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

doi:10.3390/e21020106

CORC > 武汉岩土力学研究所 > 中科院武汉岩土力学所 > 岩土力学所知识全产出 > 期刊论文

	Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling
	He, Qingfeng ; Xu, Zhihao ; Li, Shaojun ; Li, Renwei ; Zhang, Shuai ; Wang, Nianqin ; Binh Thai Pham ; Chen, Wei
刊名	ENTROPY
	2019
卷号	21 期号:2 页码:-
关键词	rotation forest credal decision tree ensemble model machine learning landslide
DOI	10.3390/e21020106
英文摘要	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.
WOS研究方向	Physics
语种	英语
WOS记录号	WOS:000460742200007
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/15049]
专题	岩土力学所知识全产出_期刊论文
作者单位	1.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China; 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Hubei, Peoples R China; 3.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	He, Qingfeng,Xu, Zhihao,Li, Shaojun,et al. Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling[J]. ENTROPY,2019,21(2):-.
APA	He, Qingfeng.,Xu, Zhihao.,Li, Shaojun.,Li, Renwei.,Zhang, Shuai.,...&Chen, Wei.(2019).Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling.ENTROPY,21(2),-.
MLA	He, Qingfeng,et al."Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling".ENTROPY 21.2(2019):-.