基于机器学习的Shapelet变换从测风记录自动识别雷暴
Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform
摘要：
雷暴的检测对于风灾社区来说非常重要，可以更好地了解极端风场特性和相关的风致荷载对结构的影响。本文有助于这一努力，提出了一个新的研究过程中，使用机器学习技术，独立的风统计参数，自主识别和分离雷暴包含高频采样连续风速测量的大型数据库。在这种情况下，Shapelet变换的使用，提出了识别关键的个人属性独特的极端风事件的基础上相似的形状，他们的时间序列。这种新颖的基于形状的表示与机器学习算法相结合时，产生了一个实用的事件检测过程与最小的领域的专业知识。在本文中，Shapelet变换与随机森林分类识别雷暴1年的数据，从14个超声波风速计，是一个广泛的原位风监测网的一部分，在地中海北部港口。使用这种方法确定了235个与雷暴相关的非平稳记录。结果导致增强池的雷暴数据更全面地了解各种各样的雷暴，以前没有检测到使用传统的阵风因子为基础的方法。
英文摘要：
Detection of thunderstorms is important to the wind hazard community to better understand extreme winds field characteristics and associated wind induced load effects on structures. This paper contributes to this effort by proposing a new course of research that uses machine learning techniques, independent of wind statistics based parameters, to autonomously identify and separate thunderstorms from large databases containing high frequency sampled continuous wind speed measurements. In this context, the use of Shapelet transform is proposed to identify key individual attributes distinctive to extreme wind events based on similarity of shape of their time series. This novel shape based representation when combined with machine learning algorithms yields a practical event detection procedure with minimal domain expertise. In this paper, the shapelet transform along with Random Forest classifier is employed for the identification of thunderstorms from 1 year of data from 14 ultrasonic anemometers that are a part of an extensive in situ wind monitoring network in the Northern Mediterranean ports. A collective total of 235 non-stationary records associated with thunderstorms were identified using this method. The results lead to enhancing the pool of thunderstorm data for more comprehensive understanding of a wide variety of thunderstorms that have not been previously detected using conventional gust factor-based methods.
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