Havzaların Benzerliklerini Tanımlamada Alternatif Bir Yaklaşım: Hiyerarşik Kümeleme Yöntemi Uygulaması

Year 2021, , 958 - 970, 31.08.2021

Ersin Aytaç

https://doi.org/10.35414/akufemubid.870649

Cited By: 2

Abstract

Makine öğrenmesi yöntemleri günümüzde birçok alanda kullanımını yaygınlaştırmış ve yerini sağlamlaştırmıştır. Denetimli, denetimsiz ve takviyeli öğrenme olmak üzere üç ana kola ayrılan makine öğrenmesi süreçleri, araştırmacıların gözle fark edemediği bağlantıları bulmada veya uzun süreli hesaplama gerektiren durumlarda ön plana çıkmaktadırlar. Denetimsiz öğrenme yöntemleri, etiketlerinin bulunmadığı verilerdeki kalıpları veya yapıyı keşfetmek için kullanılan makine öğrenmesi yöntemleridir. Hiyerarşik kümeleme süreci en önde gelen denetimsiz öğrenme yöntemlerinden birisidir. Bu çalışma havzaların benzerliklerini tanımlama da kullandığımız sürece alternatif bir yöntem sunmak amacıyla yürütülmüştür. Önerilen yöntemin avantajları arasında veri setinde yer alan tüm havzaların birbirleri ile olan ilişkilerini ortaya koyması, veri setindeki gürültüye daha az duyarlı olması, az havza içeren uygulamalarda daha kullanışlı olması ve küme içi tutarlılığı sağlamada araştırmacıya esneklik tanımasıdır. Çalışmada Türkiye’nin kuzeyinde bulunan bazı havzaların hidrolojik müdahale birimleri (HRU) görüntüleri ve hiyerarşik kümeleme yaklaşımı kullanılarak kümelenmesi incelenmiştir ve birbirine en çok benzeyen iki havzanın Ereğli ve Çaykıyı havzaları olduğu anlaşılmıştır. Havzaların birbirleri ile olan ilişkilerini ortaya çıkarmak için mesafe matrisi hazırlanmıştır. Ayrıca bağımsız kümeler oluşturmak için dendrogramın kesme mesafesi seçiminde dört farklı istatistiksel yaklaşım kullanılmıştır. İstatistiki yöntemlerin önerdiği küme sayıları içerisine kalmak şartı ve küme içi homojenliği korumak amacıyla 6 ayrı küme oluşturulmuş ve havzaların kümelere bağlı dağılımı gösterilmiştir. Bu çalışma havzaların HRU görüntülerine göre hidrolojik benzerliklerine dayanarak kümelenmelerinde alternatif bir bakış açısı sunmaktadır.

Keywords

Hiyerarşik kümeleme, Hidrolojik müdahale birimi, havza, Kesme yüksekliği, Denetimsiz öğrenme, Havza

An Alternative Approach in Defining the Similarity of Catchments: Application of Hierarchical Clustering Method

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.

Keywords

Hierarchical clustering, Hydrological response unit, Cut-off height, Unsupervised learning, Catchment

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