Machine Learning Improves Debris Flow Warning
文献类型：期刊论文
| 作者 | Chmiel, Malgorzata3; Walter, Fabian3,5; Wenner, Michaela3,5; Zhang, Zhen1,3,4; McArdell, Brian W.5; Hibert, Clement2 |
| 刊名 | GEOPHYSICAL RESEARCH LETTERS
 |
| 出版日期 | 2021-02-16 |
| 卷号 | 48期号:3页码:11 |
| 关键词 | debris flows detection environmental seismology machine learning natural hazards |
| ISSN号 | 0094-8276 |
| DOI | 10.1029/2020GL090874 |
| 通讯作者 | Chmiel, Malgorzata(chmielm@ee.ethz.ch) |
| 英文摘要 | Automatic identification of debris flow signals in continuous seismic records remains a challenge. To tackle this problem, we use machine learning, which can be applied to continuous real-time data. We show that a machine learning model based on the random forest algorithm recognizes different stages of debris flow formation and propagation at the Illgraben torrent, Switzerland, with an accuracy exceeding 90 %. In contrast to typical debris flow detection requiring instrumentation installed in the torrent, our approach provides a significant gain in warning times of tens of minutes to hours. For real-time data from 2020, our detector raises alarms for all 13 independently confirmed Illgraben events, giving no false alarms. We suggest that our seismic machine-learning detector is a critical step toward the next generation of debris-flow warning, which increases warning times using simpler instrumentation compared to existing operational systems. |
| 资助项目 | WSL ; Canton Valais ; Swiss Military |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000620058900025 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | WSL ; Canton Valais ; Swiss Military |
| 源URL | [http://ir.imde.ac.cn/handle/131551/55942]  |
| 专题 | 中国科学院水利部成都山地灾害与环境研究所 |
| 通讯作者 | Chmiel, Malgorzata |
| 作者单位 | 1.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Surface Proc, Chengdu, Peoples R China 2.Univ Strasbourg, EOST, Inst Phys Globe Strasbourg, Strasbourg, France 3.Swiss Fed Inst Technol, Lab Hydraul Hydrol & Glaciol, Zurich, Switzerland 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Swiss Fed Inst Forest Snow & Landscape Res, Zurich, Switzerland |
| 推荐引用方式 GB/T 7714 | Chmiel, Malgorzata,Walter, Fabian,Wenner, Michaela,et al. Machine Learning Improves Debris Flow Warning[J]. GEOPHYSICAL RESEARCH LETTERS,2021,48(3):11. |
| APA | Chmiel, Malgorzata,Walter, Fabian,Wenner, Michaela,Zhang, Zhen,McArdell, Brian W.,&Hibert, Clement.(2021).Machine Learning Improves Debris Flow Warning.GEOPHYSICAL RESEARCH LETTERS,48(3),11. |
| MLA | Chmiel, Malgorzata,et al."Machine Learning Improves Debris Flow Warning".GEOPHYSICAL RESEARCH LETTERS 48.3(2021):11. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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