Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania’s Historical Regions

Abstract

Abstract 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.

Keywords

• Climate change
• Extreme weather
• Anomaly detection
• Data extracting insights