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Landslide spatial modelling using novel bivariate statistical based Naive Bayes, RBF Classifier, and RBF Network machine learning algorithms
文献类型：期刊论文
| 作者 | 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
 |
| 出版日期 | 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 |
| 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 国家重点实验室知识产出_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. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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