气候变化领域集成服务门户(CCPortal): Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania's Historical Regions

CCPortal

DOI	10.1007/s44196-024-00536-2
	Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania's Historical Regions
	Bara, Adela; Vaduva, Alin Gabriel; Oprea, Simona-Vasilica
发表日期	2024
ISSN	1875-6891
EISSN	1875-6883
起始页码	17
结束页码	1
卷号	17 期号:1
英文摘要	The extreme phenomena have been increased recently in frequency and intensity causing numerous damage that cannot be neglected by residents, local authorities and social media. More European countries are experiencing a growing number of occurrences, such as floods, powerful winds, storms, icing, and unusual temperature fluctuations. Particularly, the year 2023 has recorded the highest temperatures in the history of humanity. In this research, we compile a dataset that combines news reports with numerical data pertaining to weather conditions and air quality at the historical region level in Romania. We examine the news and recorded data spanning the years from 2009 to 2023 using anomaly detection and clustering techniques to compare the results. Specifically, we employ Isolation Forest and Autoencoders to identify anomalies within the data that are further clustered to analyse the detection process. We explore the occurrence frequency and duration of daily simultaneous extreme weather events over the years, conducting statistical tests like the Mann-Kendall test to discern trends in the extreme phenomena. The findings reveal statistically significant increasing trends in the incidence of heatwaves, storms and floods. When we set the Mean Squared Error (MSE) threshold to 95%, both methods detect nearly 16% of the anomalies, and this figure rises to over 25% when the MSE threshold is set to 90%. An analysis of anomalies at the regional level indicates that most anomalies are detected in the Transylvania and Muntenia regions, while the Banat region experiences the lowest level of anomalies.
英文关键词	Climate change; Extreme weather; Anomaly detection; Data extracting insights
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001233909600001
来源期刊	INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288866
作者单位	Bucharest University of Economic Studies
推荐引用方式 GB/T 7714	Bara, Adela,Vaduva, Alin Gabriel,Oprea, Simona-Vasilica. Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania's Historical Regions[J],2024,17(1).
APA	Bara, Adela,Vaduva, Alin Gabriel,&Oprea, Simona-Vasilica.(2024).Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania's Historical Regions.INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS,17(1).
MLA	Bara, Adela,et al."Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania's Historical Regions".INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 17.1(2024).