Unsupervised learning approach in defining the similarity of catchments: Hydrological response unit based k-means clustering, a demonstration on Western Black Sea Region of Turkey

Abstract This study investigated the similarity of the catchments with the k-means clustering method by using the hydrological response unit (HRU) images of 33 catchments located in the Western Black Sea Region of Turkey. HRUs are the unit cells in hydrological models and these units are important because the same HRUs have the same hydrological behavior regarding weather inputs and water runoff. Catchments that reside inside a cluster will have high hydrological similarity, the catchments of two separate clusters would be dissimilar to each other. With the help of the clustered catchments, an elimination process can be conducted that can save time and effort in basin selection for future hydrological studies. In the study, the basic process sequence was carried out in 5 steps. These steps were creating HRUs, assigning a color to HRUs, creating HRU images, image embedding, and k-means clustering respectively. Silhouette and multidimensional scaling plots were sketched to visually examine the quality of intra-cluster distributions. Considering the silhouette score values, the optimum number of clusters was determined as 8, and the clustered catchments were illustrated on the study area.