Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites

Alper Bozkurt and Ferhat Şeker ()
Additional contact information
Alper Bozkurt: Department of Tourism Management, Business Faculty, Adana Alparslan Türkeş Science and Technology University, 01250 Adana, Türkiye
Ferhat Şeker: Department of Tourism Management, Business Faculty, Adana Alparslan Türkeş Science and Technology University, 01250 Adana, Türkiye

Sustainability, 2023, vol. 15, issue 17, 1-17

Abstract: The classification of the United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Sites (WHS) is essential for promoting sustainable tourism and ensuring the long-term conservation of cultural and natural heritage sites. Therefore, two commonly used techniques for classification problems, multilayer perceptron (MLP) and radial basis function (RBF) neural networks, were utilized to define the pros and cons of their applications. Then, according to the findings, both correlation attribute evaluator (CAE) and relief attribute evaluator (RAE) identified the region and date of inscription as the most prominent features in the classification of UNESCO WHS. As a result, a trade-off condition arises when classifying a large dataset for sustainable tourism between MLP and RBF regarding evaluation time and accuracy. MLP achieves a slightly higher accuracy rate with higher processing time, while RBF achieves a slightly lower accuracy rate but with much faster evaluation time.

Keywords: artificial intelligence; neural networks; multilayer perceptron (MLP); radial basis function (RBF); sustainable tourism; UNESCO World Heritage Sites (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/17/13031/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/17/13031/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:17:p:13031-:d:1228300

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().