Machine learning approaches for anomalous storm pattern identification

Abstract

Anomaly detection is used to explore the link between data-driven anomalous storms and their socio-economic impact on countries within the North-West Pacific. Three anomaly detection models are trialled using three distinct algorithms on the storm tracks and temperature profiles of storms. A feature-based comparison of the top 5% of anomalous storms from each model is used to reveal variations in anomalous storm activity. Further to this, the socio-economic impact of the anomalous storms is assessed, revealing a link between the anomalous behaviour of storms and the impact experienced by countries on their path. A final cross-comparison shows that the k-Nearest Neighbour and Isolation Forest algorithms succeeded at identifying high-impacting storms. However, the agglomerative clustering model found many unique storms that had low impact. This highlights the importance of considering both trajectory and temperature in determining the severity and impact of erroneous storms. HIGHLIGHTS Anomaly detection models found a link between anomalous nature and high socio-economic impact.; K-Nearest Neighbour and Isolation Forest algorithms identified high-impact storms.; Contradicting trajectory and temperature patterns were highlighted by the algorithms and were linked to high impact.;

Keywords

• anomaly detection
• clustering
• isolation forest
• k-nearest neighbour
• socio-economic impact
• storm classification