气候变化领域集成服务门户(CCPortal): Bagging-based machine learning algorithms for landslide susceptibility modeling

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DOI	10.1007/s11069-021-04986-1
	Bagging-based machine learning algorithms for landslide susceptibility modeling
	Zhang T.; Fu Q.; Wang H.; Liu F.; Wang H.; Han L.
发表日期	2021
ISSN	0921030X
英文摘要	Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree, support vector machine and classification regression tree (CART) and were integrated with bagging strategy. Then, these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. Among them the Bag-CART model has the highest AUC value of 0.874; however, the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Bagging Best-first decision tree Classification and regression tree Functional tree Landslide susceptibility Support vector machine
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206710
作者单位	Key Laboratory of Degraded and Unused Land Consolidation Engineering, The Ministry of Natural Resources, Xi’an, Shaanxi, China; Institute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Co., Ltd., Xi’an, Shaanxi, China; Shaanxi Provincial Land Engineering Construction Group Land Survey Planning and Design Institute Co., Ltd., Xi’an, Shaanxi, China; Hanzhong Branch of Shaanxi Land Engineering Construction Group Co., Ltd., Han Zhong, Shaanxi, China; School of Land Engineering, Chang’an University, Xi’an, Shaanxi, China
推荐引用方式 GB/T 7714	Zhang T.,Fu Q.,Wang H.,et al. Bagging-based machine learning algorithms for landslide susceptibility modeling[J],2021.
APA	Zhang T.,Fu Q.,Wang H.,Liu F.,Wang H.,&Han L..(2021).Bagging-based machine learning algorithms for landslide susceptibility modeling.Natural Hazards.
MLA	Zhang T.,et al."Bagging-based machine learning algorithms for landslide susceptibility modeling".Natural Hazards (2021).