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Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling
文献类型：期刊论文
| 作者 | Chen, Wei; Shahabi, Himan; Shirzadi, Ataollah; Hong, Haoyuan; Akgun, Aykut; Tian, Yingying; Liu, Junzhi; Zhu, A-Xing; Li, Shaojun |
| 刊名 | BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
 |
| 出版日期 | 2019 |
| 卷号 | 78期号:6页码:4397-4419 |
| 关键词 | Landslides Bivariate models Kernel logistic regression GIS China |
| ISSN号 | 1435-9529 |
| DOI | 10.1007/s10064-018-1401-8 |
| 英文摘要 | Globally, and in China, landslides constitute one of the most important and frequently encountered natural hazard events. In the present study, landslide susceptibility evaluation was undertaken using novel ensembles of bivariate statistical-methods-based (evidential belief function (EBF), statistical index (SI), and weights of evidence (WoE)) kernel logistic regression machine learning classifiers. A landslide inventory comprising 222 landslides and 15 conditioning factors (slope angle, slope aspect, altitude, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to rivers, distance to roads, distance to faults, NDVI, land use, lithology, and rainfall) was prepared as the spatial database. Correlation analysis and selection of conditioning factors were conducted using multicollinearity analysis and classifier attribute evaluation methods, respectively. The receiver operating characteristic curve method was used to validate the models. The areas under the success rate (AUC_T) and prediction rate (AUC_P) curves and landslide density analysis were also used to assess the prediction capability of the landslide susceptibility maps. Results showed that the EBF-KLR hybrid model had the highest predictive capability in landslide susceptibility assessment (AUC values of 0.814 and 0.753 for the training and validation datasets, respectively; AUC_T value of 0.8511 and AUC_P value of 0.7615), followed in descending order by the SI-KLR and WoE-KLR hybrid models. These findings indicate that hybrid models could improve the predictive capability of bivariate models, and that the EBF-KLR is a promising hybrid model for the spatial prediction of landslides in susceptible areas. |
| WOS研究方向 | Engineering ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000482240400037 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/14905]  |
| 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.Shandong Univ Sci & Technol, Shandong Prov Key Lab Deposit Mineralizat & Sedim, Qingdao 266590, Shandong, Peoples R China; 3.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj, Iran; 4.Univ Kurdistan, Fac Nat Resources, Dept Rangeland & Watershed Management, Sanandaj, Iran |
| 推荐引用方式 GB/T 7714 | Chen, Wei,Shahabi, Himan,Shirzadi, Ataollah,et al. Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling[J]. BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT,2019,78(6):4397-4419. |
| APA | Chen, Wei.,Shahabi, Himan.,Shirzadi, Ataollah.,Hong, Haoyuan.,Akgun, Aykut.,...&Li, Shaojun.(2019).Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling.BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT,78(6),4397-4419. |
| MLA | Chen, Wei,et al."Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling".BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 78.6(2019):4397-4419. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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