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Bozkurt, A.; Şeker, F. Heritage and Artificial Neural Networks. Encyclopedia. Available online: https://encyclopedia.pub/entry/48963 (accessed on 26 December 2024).
Bozkurt A, Şeker F. Heritage and Artificial Neural Networks. Encyclopedia. Available at: https://encyclopedia.pub/entry/48963. Accessed December 26, 2024.
Bozkurt, Alper, Ferhat Şeker. "Heritage and Artificial Neural Networks" Encyclopedia, https://encyclopedia.pub/entry/48963 (accessed December 26, 2024).
Bozkurt, A., & Şeker, F. (2023, September 08). Heritage and Artificial Neural Networks. In Encyclopedia. https://encyclopedia.pub/entry/48963
Bozkurt, Alper and Ferhat Şeker. "Heritage and Artificial Neural Networks." Encyclopedia. Web. 08 September, 2023.
Heritage and Artificial Neural Networks
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This entry is adapted from the peer-reviewed paper 10.3390/su151713031

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.

artificial intelligence neural networks multilayer perceptron (MLP)

1. Introduction

United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Sites (WHS) are of immense cultural, historical, and natural significance, and they are among the most precious treasures of our planet. As of January 2023, there are a total of 1157 world heritage sites located across 167 countries, of which 900 are cultural, 218 are natural, and 39 are mixed properties [1]. These sites are carefully selected and protected by UNESCO because of their outstanding universal value. They are landmarks of human achievement, places of exceptional beauty, and habitats for rare and endangered species. Tourism plays a vital role in supporting these sites, both in terms of their conservation and their economic and social development [2]. Visitors from all over the world come to experience the unique qualities of these sites, to learn about their history and culture, and to appreciate their natural beauty. This type of tourism generates revenue that can be used to support the preservation and administration of the sites, as well as to create jobs and economic opportunities for local communities. However, the association between tourism and UNESCO WHS is complex. While tourism can provide significant benefits, it can also have negative impacts on these sites, such as overcrowding, environmental damage, and changes to local culture. Therefore, it is a crucial task for researchers and scientists to provide and manage tourism sustainability by the conservation of world heritage sites as a top priority. UNESCO WHS are not only valuable in terms of their cultural and natural significance but also for their potential for sustainable tourism. Through careful management and responsible tourism practices, these sites can continue to be preserved for future generations to enjoy.
UNESCO WHS represent invaluable cultural, historical, and natural assets that are carefully selected and protected due to their outstanding universal value. With the increasing importance of sustainable tourism, the role of artificial neural networks (ANNs) in analyzing and managing these sites has emerged as a promising area of research. By harnessing the power of AI, ANNs can contribute to the classification, conservation, and sustainable development of UNESCO World Heritage Sites. While there have been many studies [3][4][5][6][7][8][9][10][11][12][13][14][15] conducted on these sites using traditional research methods, the classification of these structures using artificial intelligence (AI) still offers a unique perspective.
AI can play a notable role in classifying UNESCO WHS in terms of cultural, natural, or mixed categories for sustainable tourism. There are myriad advantages of AI techniques. Firstly, AI can quickly and accurately analyze vast amounts of data and images to classify world heritage sites based on their cultural, natural, or mixed attributes. This process can be time-consuming and prone to human error, but AI algorithms can perform the task much faster and more consistently. Secondly, by analyzing images and data, AI can help to identify the unique cultural or natural features of a heritage site that may not be immediately apparent to visitors. This information can be used to create more informative and engaging tourism experiences that highlight the unique features of each site. Thirdly, AI can help to identify the most popular and visited heritage sites, which can help tourism officials plan sustainable tourism strategies. With this information, officials can develop plans that manage visitor flows, reduce congestion, and preserve the natural and cultural integrity of the sites. Additionally, by analyzing visitor data and preferences, AI can help to personalize tourism experiences for visitors, providing tailored recommendations for attractions and activities based on their interests and needs. Last but not least, the use of AI in classifying UNESCO WHS can lead to more sustainable tourism practices that protect and preserve these important cultural and natural resources for future generations.

2. Heritage and Artificial Neural Networks

WHS are landmarks, areas, or buildings that have been designated as having cultural, historical, scientific, or other forms of significance by UNESCO and thus deserving of preservation for future generations. Sustainable tourism, on the other hand, refers to the responsible use of tourism resources in order to protect and conserve the environment, as well as to enhance the well-being of local communities. Several studies on the relationship between WHS and sustainable tourism have been conducted. Shao [16] conducted one such study, which investigated the impact of tourism on the cultural heritage conservation of Lijiang Old Town, a world heritage site in China. The study discovered that tourism development had a positive impact on the site’s conservation, but it also highlighted the need for sustainable management practices to minimize negative impacts. Peric et al. [17] investigated the role of WHS in sustainable tourism development in EU countries. The study discovered that world heritage sites had a significant positive impact on local economies and that sustainable tourism practices were critical for preserving the sites’ integrity.
Moreover, Dans and Gonzalez [18] aimed to identify the factors that constitute the public merit of heritage regarding sustainable tourism and showcased the social extent of the world heritage site of Altamira, Spain. Additionally, Zhang et al. [19] investigated factors impacting tourists’ conservation intentions for a Chinese world heritage site. According to the findings of the study, the straight affirmative impact of place attachment on behavioral intention is a crucial emotional element that supports behavioral intention. To sum up, the literature review highlights the importance of sustainable tourism practices for the preservation and administration of WHS. The studies suggest that sustainable tourism can have positive economic, social, and environmental impacts, and can contribute to the preservation of cultural heritage. It is crucial for tourism stakeholders to prioritize sustainable tourism practices in the development and management of WHS in order to ensure their long-term sustainability and preservation for future generations.
The relationship between artificial intelligence (AI) and WHS in the framework of sustainable tourism has been the subject of several recent studies. These studies, conducted by Ramos-Soler et al. [20], Park et al. [21], Li et al. [22], Mangut and Mallo [23], and Alsahafi et al. [24], explore the potential of AI to promote sustainable tourism at world heritage sites and other cultural heritage destinations. The authors highlight the benefits of using AI for visitor management, cultural heritage preservation, and sustainable tourism planning, while also acknowledging the challenges of implementing AI in the tourism industry. They identify opportunities for using AI to optimize tourism management, reduce negative impacts on heritage sites, and support sustainable tourism development. However, further research is needed to fully realize the potential of AI and to address ethical and social implications. These studies highlight the potential of AI to support sustainable tourism at WHS and other cultural heritage destinations.
Optimization algorithms play a crucial role in the application of ANNs for solving static optimization problems. While many ANN algorithms have been developed for static optimization, such as the Hopfield network (HN) and its derivatives, most of them do not involve a training procedure to adapt the weights of the networks [25]. To overcome this limitation, various optimization algorithms have been introduced in the design of neural networks, including particle swarm optimization and evolutionary algorithms [26]. These algorithms have been proven to be feasible and effective in optimizing neural networks. In the context of UNESCO WHS, optimization algorithms can be used to analyze and optimize various aspects, such as the impact of sea-level rise on cultural heritage sites [27]. Additionally, optimization algorithms can be applied to improve the accuracy of estimating tree diameter in historic gardens, which are UNESCO WHS [28]. By incorporating optimization algorithms into the design and analysis of ANNs, researchers can enhance the performance and applicability of these networks in solving complex optimization problems.

References

  1. UNESCO. World Heritage Centre. World Heritage List. 2023. Available online: https://whc.unesco.org/en/list (accessed on 1 May 2023).
  2. Gentry, K.; Smith, L. Critical heritage studies and the legacies of the late-twentieth century heritage canon. Int. J. Herit. Stud 2019, 25, 1148–1168.
  3. Macheka, M.T. Great Zimbabwe World Heritage Site and sustainable development. J. Cult. Herit. Manag. Sustain. Dev. 2016, 6, 226–237.
  4. Falk, M.; Hagsten, E. Visitor flows to World Heritage Sites in the era of Instagram. J. Sustain. Tour. 2021, 29, 1547–1564.
  5. Tritto, A. Environmental management practices in hotels at world heritage sites. J. Sustain. Tour. 2020, 28, 1911–1931.
  6. Maruyama, N.; Woosnam, K. Representation of “mill girls” at a UNESCO World Heritage Site in Gunma, Japan. J. Sustain. Tour. 2021, 29, 277–294.
  7. Burbano, D.; Meredith, T. Effects of tourism growth in a UNESCO World Heritage Site: Resource-based livelihood diversification in the Galapagos Islands, Ecuador. J. Sustain. Tour. 2021, 29, 1270–1289.
  8. Pérez-Gálvez, J.; Medina-Viruel, M.; Jara-Alba, C.; López-Guzmán, T. Segmentation of food market visitors in World Heritage Sites. Case study of the city of Córdoba (Spain). Curr. Issues Tour. 2021, 24, 1139–1153.
  9. Carreira, V.; González-Rodríguez, M.; Díaz-Fernández, M. The relevance of motivation, authenticity and destination image to explain future behavioural intention in a UNESCO World Heritage Site. Curr. Issues Tour. 2022, 25, 650–673.
  10. Gao, Y.; Fang, M.; Nan, Y.; Su, W. World Heritage Site inscription and city tourism attractiveness on national holidays: New evidence with migration big data from China. Curr. Issues Tour. 2022, 26, 1956–1973.
  11. Volgger, M.; Taplin, R. The impact of national park and UNESCO world heritage site designations on visit intentions: Evidence from a randomised experiment. J. Sustain. Tour. 2022, 11, 1–18.
  12. Santos-Iglesia, C.; Fernández-Arias, P.; Antón-Sancho, A.; Vergara, D. Energy Consumption of the Urban Transport Fleet in UNESCO World Heritage Sites: A Case Study of Ávila (Spain). Sustainability 2022, 14, 5641.
  13. Nian, S.; Liu, F.; Chen, M.; Cheng, G. Satisfaction of Tourism Communities in World Heritage Sites Based on Residents’ Perceptions—Study Area of Mount Sanqingshan National Park, PRC. Sustainability 2023, 15, 533.
  14. Ghimire, D.; Gautam, P.; Karki, S.; Ghimire, J.; Takagi, I. Small Business and Livelihood: A Study of Pashupatinath UNESCO Heritage Site of Nepal. Sustainability 2023, 15, 612.
  15. Aziz, N.; Ariffin, N.; Ismail, N.; Alias, A. Community Participation in the Importance of Living Heritage Conservation and Its Relationships with the Community-Based Education Model towards Creating a Sustainable Community in Melaka UNESCO World Heritage Site. Sustainability 2023, 15, 1935.
  16. Shao, Y. Conservation and Sustainable Development of Human-inhabited World Heritage Site: Case of World Heritage Lijiang Old Town. Built Herit. 2017, 1, 51–63.
  17. Perić, B.; Šimundić, B.; Muštra, V.; Vugdelija, M. The Role of UNESCO Cultural Heritage and Cultural Sector in Tourism Development: The Case of EU Countries. Sustainability 2021, 13, 5473.
  18. Dans, E.; Gonzalez, P. Sustainable tourism and social value at World Heritage Sites: Towards a conservation plan for Altamira, Spain. Ann. Tour. Res. 2019, 74, 68–80.
  19. Zhang, H.; Xiong, K.; Fei, G.; Jin, A.; Zhang, S. Factors Influencing the Conservation Intentions of Visitors to a World Heritage Site: A Case Study of Libo Karst. Sustainability 2023, 15, 5370.
  20. Ramos-Soler, I.; Martínez-Sala, A.; Campillo-Alhama, C. ICT and the Sustainability of World Heritage Sites. Analysis of Senior Citizens’ Use of Tourism Apps. Sustainability 2019, 11, 3203.
  21. Park, S.; Namho, C.; Lee, W. Preserving the Culture of Jeju Haenyeo (Women Divers) as a Sustainable Tourism Resource. Sustainability 2020, 12, 10564.
  22. Li, D.; Du, P.; He, H. Artificial Intelligence-Based Sustainable Development of Smart Heritage Tourism. Wirel. Commun. Mob. Comput. 2022, 2022, 5441170.
  23. Mangut, M.; Mallo, M. The Application of Geospatial Information Technology in Heritage Tourism in Plateau State, Nigeria. In Cultural Sustainable Tourism. Advances in Science, Technology & Innovation; Vujicic, M.D., Kasim, A., Kostopoulou, S., Chica Olmo, J., Aslam, M., Eds.; Springer: Cham, Switzerland, 2022.
  24. Alsahafi, R.; Alzahrani, A.; Mehmood, R. Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations. Sustainability 2023, 15, 4166.
  25. Serpen, G.; Patwardhan, A.; Geib, J. The Simultaneous Recurrent Neural Network for Addressing the Scaling Problem in Static Optimization. Int. J. Neur. Syst 2001, 5, 477–487.
  26. Ding, S.; Su, C.; Yu, J. An Optimizing Bp Neural Network Algorithm Based on Genetic Algorithm. Artif. Intell. Rev. 2011, 2, 153–162.
  27. Marzeion, B.; Levermann, A. Loss Of Cultural World Heritage and Currently Inhabited Places to Sea-level Rise. Environ. Res. Lett. 2014, 3, 034001.
  28. Pérez-Martín, E.; Medina, S.L.; Tejedor, T.R.H.; Pérez-Souza, M.A.; de Mata, J.A.; Ezquerra-Canalejo, A. Assessment of Tree Diameter Estimation Methods from Mobile Laser Scanning in a Historic Garden. Forests 2021, 8, 1013.
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