Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model

Abstract

Tropical cyclone (TC) track clustering plays a crucial role in understanding cyclone movement patterns, which is essential for risk assessment and disaster preparedness. This study proposes an improved SD-K-Means clustering algorithm for classifying TC tracks. Using the best-track datasets of TCs from 2000 to 2022, provided by NOAA (National Oceanic and Atmospheric Administration) and JMA (Japan Meteorological Agency), it explores the quantitative relationships between various TC features, such as latitude, longitude, and wind speed, and their motion speed and deflection angles. Based on these analyses, clustering indicators coupled with TC tracks and motion characteristics are identified. To evaluate the model’s performance, three clustering methods—standard K-Means, DTW (Dynamic Time Warping)-based K-Means, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise)—are compared using the Calinski–Harabasz (CH) index and the Davies–Bouldin Index (DBI) as evaluation metrics. The experimental results show that the SD-K-Means algorithm achieved high consistency across the majority of clustering indices, with the optimal number of clusters determined to be four. The spatial distribution of the clustering results demonstrates that SD-K-Means is effective in distinguishing different TC track patterns, providing valuable insights for regional disaster prevention and risk management efforts.