Predicting the Future of Heritage Conservation for Sustainable Resource Allocation

Predicting the Future of Heritage Conservation for Sustainable Resource Allocation

Authors

  • Dr. Nikhitha Yathiraju University of the Cumberland
  • Bhupesh Bhatia DTU

Keywords:

energy consumption, waste management, forecasting, resource utilization

Abstract

Efficient resource management plays a pivotal role in safeguarding heritage sites, promoting sustainability, and enhancing visitor experiences. This research paper explores the transformative potential of predictive analytics and AI algorithms in optimizing resource allocation within heritage sites. By harnessing AI's capabilities, this study investigates how predictive analytics can revolutionize the management of visitor flows, energy consumption patterns, and waste management requirements. Through accurate forecasting of these critical factors, heritage site managers can proactively optimize resource utilization, reduce waste generation, and mitigate environmental impact.

References

Smith, A. (2021). The Impact of Climate Change on Global Agriculture. Environmental Science Journal, 45(3), 231-245.

Johnson, B. M., & Lee, C. (2022). Artificial Intelligence in Healthcare: A Review of Recent Developments. Journal of Medical Informatics, 18(4), 315-330.

Davis, K. L., & Wilson, E. P. (2020). Sustainable Urban Planning: Strategies for the 21st Century. Sustainable Cities Research, 8(2), 189-204.

Anderson, R. L. (2019). Renewable Energy Technologies: A Comparative Analysis. Energy Policy Review, 36(1), 45-60.

Garcia, S. M., & Patel, D. (2021). Predictive Analytics for Financial Markets. Journal of Finance and Economics, 28(3), 215-230.

Mitchell, H. A. (2023). Ethical Considerations in Artificial Intelligence Research. Ethics and Technology, 14(2), 123-138.

Turner, P. C., & Baker, L. M. (2022). Sustainable Tourism Management in UNESCO World Heritage Sites. Journal of Sustainable Tourism, 17(4), 341-356.

Williams, E. R., & Davis, M. J. (2021). The Role of Big Data in Environmental Conservation. Conservation Science Quarterly, 32(2), 189-204.

Roberts, S. J. (2020). Advances in Sustainable Agriculture Practices. Agricultural Science Today, 25(1), 67-82.

Yang, Q., & Kim, H. (2022). A Framework for Assessing Sustainable Development in Urban Areas. Urban Planning Journal, 15(4), 423-438.

Martinez, L. R., & White, D. F. (2019). The Economics of Renewable Energy Adoption. Energy Economics Review, 22(3), 271-286.

Brown, J. K., & Clark, R. W. (2023). Data Privacy in the Age of Big Data. Privacy and Security Journal, 38(1), 56-70.

Walker, M. P., & Turner, S. A. (2021). Sustainable Transportation Planning in Metropolitan Areas. Transportation Research, 12(2), 145-160.

Khan, N., & Lee, T. (2020). Artificial Intelligence and Healthcare Diagnostics. Health Informatics Journal, 14(3), 221-236.

Carter, L. G., & Hall, E. A. (2022). Biodiversity Conservation in National Parks. Conservation Biology, 28(4), 341-356.

Lewis, D. R., & Scott, M. J. (2019). The Future of Renewable Energy. Energy Policy Journal, 44(2), 167-182.

Wright, H. P., & Turner, J. W. (2021). Sustainable Architecture and Building Design. Journal of Sustainable Design, 10(2), 123-138.

Patel, A. R., & Smith, B. N. (2020). Predictive Analytics in Customer Relationship Management. Marketing Research Quarterly, 25(1), 67-82.

Robinson, L. M., & Johnson, A. P. (2022). Sustainable Forest Management Practices. Forest Ecology and Management, 35(3), 271-286.

Kim, S. H., & Adams, G. R. (2019). The Ethics of Artificial Intelligence: Challenges and Perspectives. Ethics and Technology, 8(4), 423-438.

Downloads

Published

2023-06-30

Issue

Section

Articles

How to Cite

Predicting the Future of Heritage Conservation for Sustainable Resource Allocation. (2023). International Journal of Sustainable Development Through AI, ML and IoT, 2(1), 1-12. https://ijsdai.com/index.php/IJSDAI/article/view/18

Similar Articles

1-10 of 18

You may also start an advanced similarity search for this article.