Clustering of the Black Sea Region meteorological stations of Türkiye with fuzzy c-means, k-means, and silhouette index analysis methods by precipitation, temperature and wind speed

Recent years have seen a marked increase in the number of disasters caused by the effects of global climate change. In response, a range of studies have been conducted in Türkiye and worldwide with the aim of reducing the impact of climate change.The classification of regions affected by climate change into similar classes in terms of climate parameters is crucial for the application of consistent methods in studies conducted in these regions. Consequently, the formulation of effective strategies to mitigate the repercussions of climate change in these regions is contingent upon the accurate determination of the aforementioned strategy.The observation records evaluated within the scope of the study were obtained from 31 stations of the Turkish State Meteorological Service in the Black Sea Region, encompassing the period between 1982 and 2020, encompassing precipitation, temperature, and wind speed records.. The maximum number of clusters was determined as 5, the cluster analysis study was carried out by using fuzzy c-means and k-means methods for 2, 3, 4, and 5 cluster numbers according to these three data together form a matrix. The determination of the optimum cluster numbers was carried out by silhouette index analysis. For the data matrix where precipitation, temperature, and wind speed were evaluated together, the most appropriate classification was obtained by the k-means method by choosing the number of clusters as 4.