Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23762
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran
Authors: Sharafi, Siyamack
Fouladvand, Sajjad
Simpson, Ian
Alvarez, Juan Antonio B
Contact Email: i.a.simpson@stir.ac.uk
Keywords: Artificial intelligence
Environmental variables
Khorramabad Plain
One-class classification
Pattern recognition
Predictive modeling
Issue Date: Aug-2016
Publisher: Elsevier
Citation: Sharafi S, Fouladvand S, Simpson I & Alvarez JAB (2016) Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran, Journal of Archaeological Science: Reports, 8, pp. 206-215.
Abstract: Archaeologists continue to search for techniques that enable them to analyze archaeological data efficiently with artificial intelligence approaches increasingly employed to create new knowledge from archaeological data. The purpose of this paper is to investigate the application of Pattern Recognition methods in detection of buried archaeological sites of the semi-arid Khorramabad Plain located in west Iran. This environment has provided suitable conditions for human habitation for over 40,000 years. However, environmental changes in the late Pleistocene and Holocene have caused erosion and sedimentation resulting in burial of some archaeological sites making archaeological landscape reconstructions more challenging. In this paper, the environmental variables that have influenced formation of archaeological sites of the Khorramabad Plain are identified through the application of Arc GIS. These variables are utilized to create an accurate predictive model based on the application of One-Class classification Pattern Recognition techniques. These techniques can be built using data from one class only, when the data from other classes are difficult to obtain, and are highly suitable in this context. The experimental results of this paper confirm one-class classifiers, including Auto-encoder Neural Network, k-means, principal component analysis data descriptor, minimum spanning tree data descriptor, k-nearest neighbour and Gaussian distribution as promising applications in creating an effective model for detecting buried archaeological sites. Among the investigated classifiers, minimum spanning tree data descriptor achieved the best performance on the Khorramabad Plain data set. © 2016 Elsevier Ltd.
Type: Journal Article
URI: http://hdl.handle.net/1893/23762
DOI Link: http://dx.doi.org/10.1016/j.jasrep.2016.06.024
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Sharafi S, Fouladvand S, Simpson I & Alvarez JAB (2016) Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran, Journal of Archaeological Science: Reports, 8, pp. 206-215. DOI: 10.1016/j.jasrep.2016.06.024 © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Affiliation: Academic Centre for Education, Culture and Research
Academic Centre for Education, Culture and Research
Biological and Environmental Sciences
Universitat Autonoma de Barcelona

Files in This Item:
File Description SizeFormat 
AI_archaeology_Khorramabad_English Revision.pdf1.34 MBAdobe PDFUnder Embargo until 26/7/2017     Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependant on the depositor still being contactable at their original email address.

This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.