Landslide spatial modelling using novel bivariate statistical based Naive Bayes, RBF Classifier, and RBF Network machine learning algorithms

doi:10.1016/j.scitotenv.2019.01.329

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

	Landslide spatial modelling using novel bivariate statistical based Naive Bayes, RBF Classifier, and RBF Network machine learning algorithms
	He, Qingfeng ; Shahabi, Himan ; Shirzadi, Ataollah ; Li, Shaojun ; Chen, Wei ; Wang, Nianqin ; Chai, Huichan ; Bian, Huiyuan ; Ma, Jianquan ; Chen, Yingtao
刊名	SCIENCE OF THE TOTAL ENVIRONMENT
	2019
卷号	663 期号:-页码:43845
关键词	Landslide susceptibility Longhai area Naive Bayes RBF Classifier RBF Network
ISSN号	0048-9697
DOI	10.1016/j.scitotenv.2019.01.329
英文摘要	Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naive Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world. (C) 2019 Published by Elsevier B.V.
WOS研究方向	Environmental Sciences & Ecology
语种	英语
WOS记录号	WOS:000459858500001
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/14995]
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.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj, Iran; 3.Univ Kurdistan, Fac Nat Resources, Dept Rangeland & Watershed Management, Sanandaj, Iran; 4.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Hubei, Peoples R China
推荐引用方式 GB/T 7714	He, Qingfeng,Shahabi, Himan,Shirzadi, Ataollah,et al. Landslide spatial modelling using novel bivariate statistical based Naive Bayes, RBF Classifier, and RBF Network machine learning algorithms[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2019,663(-):43845.
APA	He, Qingfeng.,Shahabi, Himan.,Shirzadi, Ataollah.,Li, Shaojun.,Chen, Wei.,...&Bin Ahmad, Baharin.(2019).Landslide spatial modelling using novel bivariate statistical based Naive Bayes, RBF Classifier, and RBF Network machine learning algorithms.SCIENCE OF THE TOTAL ENVIRONMENT,663(-),43845.
MLA	He, Qingfeng,et al."Landslide spatial modelling using novel bivariate statistical based Naive Bayes, RBF Classifier, and RBF Network machine learning algorithms".SCIENCE OF THE TOTAL ENVIRONMENT 663.-(2019):43845.