Susceptibility assessment of earth fissure related to groundwater extraction using machine learning methods combined with weights of evidence
文献类型：期刊论文
| 作者 | Wei, Aihua1,2,5; Chen, Yuanyao1; Zhao, Haijun4; Liu, Zhao1,2,5; Yang, Likui1,2,5; Yan, Liangdong1,2,5; Li, Hui3 |
| 刊名 | NATURAL HAZARDS
 |
| 出版日期 | 2023-09-22 |
| 页码 | 23 |
| ISSN号 | 0921-030X |
| 关键词 | Earth fissure assessment Weights of evidence Support vector machine learning Random forest Ensemble models |
| DOI | 10.1007/s11069-023-06198-1 |
| 英文摘要 | The susceptibility of a region to the occurrence of earth fissures is often used to assess the probability of geohazards across an area. The main objective of this study is to discuss and explore machine learning methods for earth fissure susceptibility assessment, including the single machine learning method and the ensemble model. A total of ten affecting factors including elevation, slope, topographic wetness index, rainfall, drawdown of groundwater level, the thickness of Quaternary sediments, distance from rivers, distance to faults, normalized difference vegetation index, and land use were selected. The weight of evidence (WoE) method was first used to determine the quantitative relationship between an earth fissure and its related parameters. The WoE, support vector machine learning combined with the WoE (SVM +WoE), and the random forest combined with the WoE (RF+ WoE) model were then used to classify earth fissure susceptibility. The area under the curve and root-mean-squared error was used to evaluate the three methods and to determine the most optimal approach for earth fissure susceptibility map. The results indicated that the RF+ WoE model had the highest predictive accuracy, followed by the SVM+WoE and the WoE models. The study area was finally classified into regions with very high, high, moderate, low, and very low susceptibility, accounting for 11.20%, 15.66%, 24.13%, 32.60%, and 16.07% of the area. Susceptibility mapping can apply machine learning methods combined with the WoE method for earth fissure assessment. |
| WOS关键词 | SUPPORT VECTOR MACHINE ; LAND SUBSIDENCE ; OF-EVIDENCE ; PREDICTION ; MODELS ; BASIN ; GIS |
| 资助项目 | This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper.[D2022403032] ; Hebei Natural Science Foundation |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences ; Water Resources |
| 语种 | 英语 |
| 出版者 | SPRINGER |
| WOS记录号 | WOS:001070355600004 |
| 资助机构 | This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper. ; This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper. ; Hebei Natural Science Foundation ; Hebei Natural Science Foundation ; This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper. ; This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper. ; Hebei Natural Science Foundation ; Hebei Natural Science Foundation ; This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper. ; This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper. ; Hebei Natural Science Foundation ; Hebei Natural Science Foundation ; This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper. ; This research was supported by Hebei Natural Science Foundation (D2022403032). The authors are highly indebted to the data provider, the anonymous reviewers, and the editors, who significantly improved the quality of the paper. ; Hebei Natural Science Foundation ; Hebei Natural Science Foundation |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/110793]  |
| 专题 | 地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室 |
| 通讯作者 | Yan, Liangdong |
| 作者单位 | 1.Hebei GEO Univ, Sch Water Resources & Environm, Shijiazhuang 050031, Peoples R China 2.Hebei GEO Univ, Hebei Prov Collaborat Innovat Ctr Sustainable Util, Shijiazhuang 050031, Peoples R China 3.Hebei GEOenvironm Monitoring, Shijiazhuang 050011, Peoples R China 4.Chinese Acad Sci, Key Lab Shale Gas & Geoengn, Inst Geol & Geophys, Beijing 100029, Peoples R China 5.Hebei GEO Univ, Hebei Prov Key Lab Sustained Utilizat & Dev Water, Shijiazhuang 050031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wei, Aihua,Chen, Yuanyao,Zhao, Haijun,et al. Susceptibility assessment of earth fissure related to groundwater extraction using machine learning methods combined with weights of evidence[J]. NATURAL HAZARDS,2023:23. |
| APA | Wei, Aihua.,Chen, Yuanyao.,Zhao, Haijun.,Liu, Zhao.,Yang, Likui.,...&Li, Hui.(2023).Susceptibility assessment of earth fissure related to groundwater extraction using machine learning methods combined with weights of evidence.NATURAL HAZARDS,23. |
| MLA | Wei, Aihua,et al."Susceptibility assessment of earth fissure related to groundwater extraction using machine learning methods combined with weights of evidence".NATURAL HAZARDS (2023):23. |
入库方式： OAI收割
来源：地质与地球物理研究所
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