Predicting the Number of Visitors with Artificial Neural Networks to Support Strategic Decision-Making for Science Centers

Year 2024, Volume: 13 Issue: 3, 836 - 843, 26.09.2024

Ali Çetinkaya , Havva Kırgız , Ferzan Kara

https://doi.org/10.17798/bitlisfen.1501209

Abstract

Accurately predicting visitor attendance has become increasingly vital for science centers to optimize operations, improve visitor experiences, and stay competitive in attracting and engaging global audiences. As the demand for advanced predictive analytics grows, this study explores the use of artificial neural networks (ANNs) to forecast visitor numbers at science centers. In order to achieve this objective, data pertaining to the number of visitors to the Konya Science Centre was utilized. By analyzing a dataset of ten input factors, such as weather conditions and past visitor behavior, the study develops predictive models capable of accurately estimating future attendance patterns. The best-performing model, utilizing Bayesian regularization, 0.91444 for the training set, 0.25119 for the test set, and 0.91342 overall. These findings underscore the transformative potential of predictive analytics in science center management. Leveraging machine learning techniques, the study provides valuable insights into visitor preferences and behavior. This knowledge can empower science centers to make data-driven decisions, optimize resource allocation, and adapt their offerings to meet the evolving needs of their target audience. Ultimately, the study highlights how predictive analytics can enhance the long-term sustainability and global competitiveness of science center operations.

Keywords

Science center, Prediction, Neural networks, Visitor, Bayesian, Machine learning

References

• [1] E. Y. Kuyrukçu, "The importance of iconic buildings for city image: Konya Science Center example," ICONARP International Journal of Architecture and Planning, vol. 6, no. 2, pp. 461-481, 2018.
• [2] TÜBİTAK. "About the Call for Large Scale Science Centres." https://bilimmerkezleri.tubitak.gov.tr/Icerik/buyuk-olcekli-bilim-merkezi-projeleri-cagrisi-hakkinda-146 (accessed 11.06.2024.
• [3] Ö. Alcan, M. Demir, and Y. Alcan, "Prediction of the Numbers of Visitors at the Sinop Museums by Artificial Neural Networks," Karadeniz Fen Bilimleri Dergisi, vol. 9, no. 1, pp. 70-81, 2019.
• [4] M. Çuhadar, İ. Güngör, and A. Göksu, "Turizm Talebinin Yapay Sinir Ağları İle Tahmini Ve Zaman Serisi Yöntemleri İle Karşılaştırmalı Analizi: Antalya İline Yönelik Bir Uygulama," Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi,, vol. 14, no. 1, pp. 99-114, 2009.
• [5] S. Wahyuni, A. D. Nasution, and H. Hermansyah, "Optimization Of Data Mining In Predicting Tourist Visits At The Deli Serdang Museum," in International Conference on Sciences Development and Technology, 2023, vol. 3, no. 1, pp. 115-120.
• [6] S. Öztemiz and M. A. Tekindal, "Forecasting the number of visitors of the museums and ruins by Using time series analysis: The case of Turkey," Türk Kütüphaneciliği, vol. 35, no. 2, pp. 232-248, 2021.
• [7] J. Beresford, "Mind the Gap: Prediction and Performance in Respect to Visitor Numbers at the New Acropolis Museum," Museum & Society, vol. 12, no. 3, pp. 171-190, 2014.
• [8] G. Trinh and D. Lam, "Understanding the attendance at cultural venues and events with stochastic preference models," Journal of Business Research, vol. 69, no. 9, pp. 3538-3544, 2016.
• [9] E. S. Silva, H. Hassani, S. Heravi, and X. Huang, "Forecasting tourism demand with denoised neural networks," Annals of Tourism Research, vol. 74, pp. 134-154, 2019.
• [10] N. Yap, M. Gong, R. K. Naha, and A. Mahanti, "Machine learning-based modelling for museum visitations prediction," in 2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020: IEEE, pp. 1-7.
• [11] W. Limpornchitwilai and P. Suksompong, "Analysis of visitor data prediction of National Science Museum Thailand (NSM)," Thammasat University, 2021.
• [12] B. Ejstrud, "Visitor numbers and feasibility studies. Predicting visitor numbers to danish open‐air museums using GIS and multivariate statistics," Scandinavian Journal of Hospitality and Tourism, vol. 6, no. 4, pp. 327-335, 2006.
• [13] A. Z. Abang Abdurahman, W. F. Wan Yaacob, S. A. Md Nasir, S. Jaya, and S. Mokhtar, "Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia," Sustainability, vol. 14, no. 5, p. 2735, 2022.
• [14] J. Bravo, R. Alarcón, C. Valdivia, and O. Serquén, "Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru," Sustainability, vol. 15, no. 11, p. 8967, 2023.
• [15] X. Liu, Y.-n. Chen, Z. Qiu, and M.-r. Chen, "Forecast of the tourist volume of Sanya city by XGBoost model and GM model," in 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2019: IEEE, pp. 166-173.
• [16] G. Dong and H. Liu, Feature engineering for machine learning and data analytics. CRC press, 2018.