Havzaların Benzerliklerini Tanımlamada Alternatif Bir Yaklaşım: Hiyerarşik Kümeleme Yöntemi Uygulaması

Abstract

Machine learning methods have widespread their use and strengthened their places in many areas. These procedures can be divided into three main branches as supervised, unsupervised, and reinforced learning and assist researchers to find connections in the data that cannot be seen or in situations that require long-term computation. Unsupervised methods are used to discover patterns or structures in data which does not have any labels. The hierarchical clustering process is one of the leading unsupervised methods. This study is an alternative approach to the process we used in defining the similarities of basins. The advantages of the proposed method are; reveals the relations between all basins, is less sensitive to noise in the data set, is more useful in applications with fewer basins, and is flexible in ensuring intra-cluster consistency. Some basins located in the north of Turkey based on their hydrological response units (HRU) images were hierarchically clustered and found out that and the two basins most similar to each other are the Ereğli and Çaykıyı catchments. A distance matrix was prepared to reveal the relations of the basins. Besides, to create independent clusters, four different statistical approaches were used to select the cut-off height of the dendrogram. To stay within the cluster numbers suggested by statistical methods and to ensure cluster homogeneity, 6 separate clusters were created. The distribution of the catchments depending on the clusters was illustrated. This study provides an alternative perspective for the clustering of basins based on HRU images.

A Topic Modeling Approach to Discover the Global and Local Subjects in Membrane Distillation Separation Process

“…Unsupervised learning techniques like clustering and dimension reduction (DR) can uncover hidden patterns in data. Reinforcement ML can learn from past experiences and find the best actions to take in an unfamiliar environment to achieve the ideal state transition for achieving a specific goal [27].…”