Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningEvent DetectionTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
The Error is the Feature: how to Forecast Lightning using a Model Prediction Error
Despite the progress within the last decades, weather forecasting is still a challenging and computationally expensive task. Current satellite-based approaches to predict thunderstorms are usually based on the analysis o…
BIG-bench Machine LearningOptical Flow EstimationWeather ForecastingMake Thunderbolts Less Frightening -- Predicting Extreme Weather Using Deep Learning
Forecasting severe weather conditions is still a very challenging and computationally expensive task due to the enormous amount of data and the complexity of the underlying physics. Machine learning approaches and especi…
Binary ClassificationDeep LearningWeather ForecastingRevisiting the probabilistic method of record linkage
In theory, the probabilistic linkage method provides two distinct advantages over non-probabilistic methods, including minimal rates of linkage error and accurate measures of these rates for data users. However, implemen…
BlockingA Semi-supervised Approach for De-identification of Swedish Clinical Text
An abundance of electronic health records (EHR) is produced every day within healthcare. The records possess valuable information for research and future improvement of healthcare. Multiple efforts have been done to prot…
De-identificationUtility Preservation of Clinical Text After De-Identification
Electronic health records contain valuable information about symptoms, diagnosis, treatment and outcomes of the treatments of individual patients. However, the records may also contain information that can reveal the ide…
De-identification