Failures in railway systems

IF 29.6 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Nature Climate Change Pub Date : 2024-10-07 DOI:10.1038/s41558-024-02157-1

Lingxiao Yan

引用次数: 0

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铁路系统故障

瑞典吕勒奥理工大学的 Khosro Soleimani-Chamkhorami 及其同事开发了一个 ML 框架，用于区分气候性故障和非气候性故障。他们收集了瑞典铁路系统 23 年来的故障报告、资产和天气信息数据集。结果表明，随机森林模型的准确率最高。他们的分析还表明，在对资产故障进行分类时，最低气温和雨雪量等气象因素最为重要。此外，故障事件发生前 24 小时是事件分类的最关键窗口。作者的研究结果表明，ML 方法可以对不同资产类型和地理区域的气候故障进行准确分类：原始参考文献：Transport.Res. Part D 135, 104371 (2024)

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来源期刊

Nature Climate Change ENVIRONMENTAL SCIENCES-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

40.30

自引率

1.60%

发文量

267

审稿时长

4-8 weeks

期刊介绍： Nature Climate Change is dedicated to addressing the scientific challenge of understanding Earth's changing climate and its societal implications. As a monthly journal, it publishes significant and cutting-edge research on the nature, causes, and impacts of global climate change, as well as its implications for the economy, policy, and the world at large. The journal publishes original research spanning the natural and social sciences, synthesizing interdisciplinary research to provide a comprehensive understanding of climate change. It upholds the high standards set by all Nature-branded journals, ensuring top-tier original research through a fair and rigorous review process, broad readership access, high standards of copy editing and production, rapid publication, and independence from academic societies and other vested interests. Nature Climate Change serves as a platform for discussion among experts, publishing opinion, analysis, and review articles. It also features Research Highlights to highlight important developments in the field and original reporting from renowned science journalists in the form of feature articles. Topics covered in the journal include adaptation, atmospheric science, ecology, economics, energy, impacts and vulnerability, mitigation, oceanography, policy, sociology, and sustainability, among others.