Management Strategies for the Conservation of Wildlife Based on the Analytic Hierarchy Process and Gray Prediction Models
- DOI
- 10.2991/978-94-6463-256-9_155How to use a DOI?
- Keywords
- gray correlation analysis; AHP; management strategies; gray prediction model
- Abstract
This paper investigates resource management methods in the Masai Mara Wildlife Reserve, proposing management strategies for the conservation of wildlife and natural resources. We utilize the Analytic Hierarchy Process (AHP) model and gray relational analysis to determine the most effective management strategies and predict their long-term trends. We optimize weight values using a stepwise quality house method and establish an AHP hierarchical analysis model. Finally, we construct a gray forecasting model to predict the data for the next 12 years of the management strategies, enabling a long-term projection. Our findings can provide insights for conservationists and policymakers for effective resource management in the Masai Mara Wildlife Reserve.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Minghuan Piao AU - Wanting Zhang AU - Chaofeng Cheng PY - 2023 DA - 2023/10/09 TI - Management Strategies for the Conservation of Wildlife Based on the Analytic Hierarchy Process and Gray Prediction Models BT - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023) PB - Atlantis Press SP - 1529 EP - 1539 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-256-9_155 DO - 10.2991/978-94-6463-256-9_155 ID - Piao2023 ER -