气候变化领域集成服务门户(CCPortal): Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods

CCPortal

DOI	10.1007/s11069-020-04089-3
	Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods
	Das S.K.; Mohanty R.; Mohanty M.; Mahamaya M.
发表日期	2020
ISSN	0921030X
起始页码	2371
结束页码	2393
卷号	103 期号:2
英文摘要	The prediction of liquefaction susceptibility for highly unbalanced database with limited and important input parameters is a crucial issue. The proposed multi-objective feature selection algorithms (MOFS) were applied to highly unbalanced databases of in situ tests: standard penetration test (SPT), cone penetration test (CPT) and shear wave velocity (Vs) test.Two multi-objective algorithms, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective symbiotic organisms search algorithm (MOSOS), were coupled with learning algorithms, artificial neural network (ANN) and multivariate adaptive regression spline (MARS) separately to effectively select the optimal parameters and simultaneously minimize the error. The obtained optimal point has approximately equal accuracy in both liquefiable and non-liquefiable conditions for training and testing. The important inputs found for models based on SPT are: (N1)60, amax and Mw; CPT: qc1, amax and CSR and Vs: Vs1, CSR, amax and Mw. The CPT-based models were found to be the most efficient. © 2020, Springer Nature B.V.
关键词	ANN Feature selection In situ tests Liquefaction MARS MOSOS Multi-objective optimization NSGA-II
英文关键词	algorithm; artificial neural network; computer simulation; earthquake prediction; in situ test; liquefaction; parameter estimation; regression analysis; S-wave; seismic velocity
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/205652
作者单位	Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand 826004, India; Department of Civil Engineering, National Institute of Technology Rourkela, Odisha, 769008, India
推荐引用方式 GB/T 7714	Das S.K.,Mohanty R.,Mohanty M.,et al. Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods[J],2020,103(2).
APA	Das S.K.,Mohanty R.,Mohanty M.,&Mahamaya M..(2020).Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods.Natural Hazards,103(2).
MLA	Das S.K.,et al."Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods".Natural Hazards 103.2(2020).