Prediction Using Different Classification Methods of Tree Species Depending on Contact Angle Values

Öz

Nowadays, in various studies, the tree type can be determined according to the different properties of a tree material such as mechanical, chemical, physical properties, anatomy, but these studies are both long-lasting and require cost. Especially knowing the wettability of wooden materials used in outdoor weather conditions gives information about the usability of the material (pool edge, sauna, siding) in these weather conditions. In this study, the determination of the type of wood material was done by artificial neural networks (ANN), support vector machines (DVM), K-nearest neighbor (K-EYK) and Naive Bayes (NB) method according to the wettability feature. Contact angles of heat treated and unheat treated cedar (Cedrus Libani), Iroko (Chlorophora excelsa), Ash (Fraxinus excelsior) and Spruce (Picea abies) samples were determined. Later, artificial neural networks (ANN), support vector machines, K-nearest neighbor and Naive bayes classification methods have been estimated. It will be fast, practical and economical with this method used in the study to determine which wood species belongs to, depending on the wettability property measured by an easy method. Determination of the tree type with dropping method easily will contribute to the restoration and strengthening works.

Anahtar Kelimeler

• Wettability
• Contact angle
• Wood Type
• Prediction
• Classification methods
• Machine learning

Temas Açısı Değerlerine Bağlı Ağaç Türlerinin Farklı Sınıflandırma Yöntemleri İle Tahmini

Öz

Günümüzde çeşitli çalışmalarda bir ağaç malzemenin mekanik, kimyasal, fiziksel özellikleri, anatomisi gibi farklı özelliklerine göre ağaç türü tespit edilebilmekle birlikte bu çalışmalar hem uzun sürmekte hem de maliyet gerektirmektedir. Özellikle dış hava koşullarında kullanılan ısıl işlem görmüş ahşap malzemelerin ıslanabilirlik özelliğinin bilinmesi malzemenin bu hava şartlarında hangi alanda (havuz kenarı, sauna, dış cephe kaplaması vb.) kullanılabilirliği hususunda bilgi vermektedir. Bu çalışmada ıslanabilirlik özelliğine göre yapay sinir ağları (YSA), destek vektör makineleri (DVM), K-en yakın komşu (K-EYK) ve Naive Bayes (NB) yöntemi ile ağaç malzemenin türünün tespiti işlemi yapılmıştır. Isıl işlem görmüş ve ısıl işlem görmemiş Sedir (Cedrus Libani), Iroko (Chlorophora excelsa), Dişbudak (Fraxinus excelsior) ve Ladin (Picea abies) numunelerin damla metodu ile temas açıları belirlenmiştir. Daha sonra yapay sinir ağları (YSA), destek vektör makineleri, K-en yakın komşu ve Naive bayes sınıflandırma metotları ile ağaç türlerinin tahmini yapılmıştır. Ahşap malzemenin kolay bir metot ile ölçülen ıslanabilirlik özelliğine bağlı olarak hangi ağaç türüne ait olduğunun belirlenmesi çalışmada kullanılan bu yöntem ile hızlı, pratik ve ekonomik olacaktır. Damlatma metodu ile ağaç türünün kolaylıkla belirlenmesi, restorasyon ve güçlendirme çalışmalarına katkı sağlayacaktır.

Anahtar Kelimeler

• Islanabilirlik
• Temas açısı
• Ağaç türü
• Tahmin
• Sınıflandırma metodları
• Makine öğrenmesi

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