# Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model
Whether a TC makes landfall, and its specific landing location, are determined by its track, and its impact extent is governed by the intensity it attains during its evolution. TC tracks are critical for evaluating the potential impact areas and intensity of storms. Classifying TC tracks helps to better understand their characteristics and activity patterns [8]. Common approaches to TC track classification include subjective identification and objective analytical methods. The subjective identification method relies heavily on expert judgment. For example, [PERSON] et al. [9] classified Northwest Pacific (NWP) TCs into four categories based on their genesis locations, activity regions, and dissipation points. However, such methods are highly dependent on human expertise, leading to instability and subjectivity in the classification results. To overcome these limitations, objective analysis methods grounded in mathematical theory have gained popularity and have become mainstream in recent years. For instance, [PERSON] [5] utilized a K-Means clustering algorithm based on the longitude and latitude of TC positions at peak intensity and prior to dissipation to classify NWP TCs into straight-moving and northward-bending tracks. However, this approach used only partial location data, thereby underutilizing the complete track information. Building on [PERSON]'s work, [PERSON] et al. [10] employed the HURDAT dataset (Atlantic Basin Historical Hurricane  
Figure 1: A map of China’s provincial administrative regions with Hainan Province highlighted in red. The black lines with purple shading are national boundaries. The solid grey lines are provincial, autonomous region or municipality boundaries. The dashed grey lines are special administrative region boundaries. The blue lines are coastlines. The red names are the names of provinces, autonomous regions or municipalities. The stars represent the capital, Beijing. The black dots and names are the provincial administrative centers and their names. Track Data) and proposed a classification method based on mass moments, including the centroid and variances (radial, latitudinal, and diagonal) of position, intensity, and wind speed. Their dynamic perspective allowed them to cluster Atlantic hurricanes into six distinct types using K-Means. [PERSON] et al. [11] took NWP TCs as the research object. Based on the work of [PERSON] et al., they improved the coefficients of five clustering indicators and strengthened the importance of track length, shape, and direction. The above studies focus only on the characteristics of the TC track itself, emphasizing the importance of physical variables, and lack consideration of the changes in physical and spatial characteristics caused by the influence of the environmental flow field during the movement of the TC. In response to the K-Means clustering algorithm's limitations--specifically, its insensitivity to temporal characteristics and inability to account for variations in track length--[PERSON] et al. [12] proposed a model based on the Finite Mixture Model (FMM). By treating all time-step positions along TC tracks as independent variables and fitting quadratic polynomial regressions, they classified Northwest Pacific TCs from 1950 to 2002 into seven categories using the JTWC (Joint Typhoon Warning Center) dataset. [PERSON] et al. [13] adopted a simple interpolation method combined with the Fuzzy C-Means (FCM) algorithm to retain the approximate track shape and classified NWP TCs into seven categories. They also introduced four validity metrics to determine the optimal number of clusters. [PERSON] et al. [14], studying TCs in the North Indian Ocean, compared K-Means and Finite Mixture Models (FMMs), concluding that both produced similar track classifications. [PERSON] et al.