Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020GL090874 |
Machine Learning improves debris flow warning | |
Mał; gorzata Chmiel; Fabian Walter; Michaela Wenner; Zhen Zhang; Brian W. McArdell; Clement Hibert | |
2020-12-29 | |
发表期刊 | Geophysical Research Letters |
出版年 | 2020 |
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 towards the next generation of debris‐flow warning, which increases warning times using simpler instrumentation compared to existing operational systems. |
领域 | 气候变化 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/311419 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Mał,gorzata Chmiel,Fabian Walter,et al. Machine Learning improves debris flow warning[J]. Geophysical Research Letters,2020. |
APA | Mał.,gorzata Chmiel.,Fabian Walter.,Michaela Wenner.,Zhen Zhang.,...&Clement Hibert.(2020).Machine Learning improves debris flow warning.Geophysical Research Letters. |
MLA | Mał,et al."Machine Learning improves debris flow warning".Geophysical Research Letters (2020). |
条目包含的文件 | 条目无相关文件。 |
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