气候变化领域集成服务门户(CCPortal): Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China

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DOI	10.1007/s11069-021-04713-w
	Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China
	Ling Q.; Zhang Q.; Zhang J.; Kong L.; Zhang W.; Zhu L.
发表日期	2021
ISSN	0921030X
起始页码	925
结束页码	946
卷号	108 期号:1
英文摘要	Prediction of landslide movement is an efficient approach in the reduction in landslide risk. However, it is also a tough task due to the scientific challenges in forecasting a sophisticated natural disaster. This paper proposes a VMD-MIC-M-KELM (variational mode decomposition-maximum information coefficient-multi-kernel extreme learning machine) technique for prediction of landslide movements. The original displacement is first decomposed into a predefined number of components by VMD. Then, the triggers of each component are selected based on MIC between subseries and influencing factors. The decomposed terms are predicted by M-KELM respectively via k-fold cross-validation. Finally, predicted total displacement is achieved by summing up all forecasting subseries. A case study of Miaodian landslide (China) is presented for validation of the developed model. The verification results demonstrate the higher ability of the approach to forecast monthly displacement for periods up to 12 months as compared to the Poly-KELM and SVR models. Thus, improved monthly predictions may be achieved with constantly updated datasets from the monitoring system, which would offer reliable information for early warning of landslide. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Displacement prediction Kernel extreme learning machine Kernel functions Maximum information coefficient Variational mode decomposition
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/206372
作者单位	School of Civil Engineering, Lanzhou University of Technology, Lanzhou Gansu, China; College of Geology Engineering and Geomatics, Chang’ an University, Xian Shaanxi, China; The First Geodetic Team of the Ministry of Natural Resources, Xian Shaanxi, China; Information Engineering University, Zhengzhou Henan, China
推荐引用方式 GB/T 7714	Ling Q.,Zhang Q.,Zhang J.,et al. Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China[J],2021,108(1).
APA	Ling Q.,Zhang Q.,Zhang J.,Kong L.,Zhang W.,&Zhu L..(2021).Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China.Natural Hazards,108(1).
MLA	Ling Q.,et al."Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China".Natural Hazards 108.1(2021).