year 8, Issue 2 (2022)
JRA 2022, 8(2): 147-163 |
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Mafi S, Dehpahlavan M, Aarab A. Classification and Typology of Iron Age II Pottery from Excavations of Qareh Tepe of Sagzabad, Qazvin using Radius, Tangent and Curvature (RTC) Functions of the Lip Profile of Pottery Vessels. JRA 2022; 8 (2) :147-163
URL: http://jra-tabriziau.ir/article-1-336-en.html
URL: http://jra-tabriziau.ir/article-1-336-en.html
1- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran
2- Department of Archaeology, Faculty of Literature and Humanities, University of Tehran, Iran ,mdehpahlavan@ut.ac.ir
3- Department of Archaeology, Faculty of Literature and Humanities, University of Tehran, Iran
2- Department of Archaeology, Faculty of Literature and Humanities, University of Tehran, Iran ,
3- Department of Archaeology, Faculty of Literature and Humanities, University of Tehran, Iran
Abstract: (2640 Views)
One of the critical tasks of archaeological studies is to illustrate in detail the geometric properties of pottery fragments. The illustrations are presented as classification and typological concepts for the potteries of a historical period. They are used to investigate the cultural and social structures of that period. Nowadays, most similar studies are conducted by numerical methods and mathematical modeling. This research examines the feasibility of using these methods and algorithms in the classification and typology of ancient potteries. What is important at first glance is introducing a set of geometric properties of the outer profile of the pottery fragments, which are entered to the algorithms in the form of RTC functions. These functions show unique features for an outer profile which are used in the classification and typology of the fragments. The classification of samples is accomplished based on RTC functions and by using an Artificial Neural Network (ANN). The samples are categorized in bowl, jug and jar classes with nearly 95% accuracy. The designed ANN is a three-layer network with one hidden layer that includes two neurons. Network outputs are numeric codes 0, 1 and 2 which refer to bowl, jug and jar profiles, respectively. In the following, by correcting the erroneous samples and out of a total of 209 pottery designs, 128 (61.2%), 42 (20.1%) and 39 (18.7%) samples were placed in bowl, jug and jar categories, respectively. After classification, the typology of the available samples within each class is also performed by the Neighbor Joining (NJ) algorithm. According to the NJ algorithm, the bowl, jug, and jar samples are distinguished by 83, 22 and 27 types, respectively. The accuracy of the typology method is also evaluated between 95 and 97%.
Technical Note: Original Research |
Subject:
Archaeometry
Received: 2022/08/27 | Accepted: 2023/02/6 | Published: 2023/02/28 | ePublished: 2023/02/28
Received: 2022/08/27 | Accepted: 2023/02/6 | Published: 2023/02/28 | ePublished: 2023/02/28
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