Study on Illegal Wildlife Trade Based on Hierarchical Analysis and Random Forest Modeling
With the goal of solving the problem of illegal wildlife trade, this study proposes a data-driven five-year project by combining hierarchical analytical modeling and random forest modeling. First, 16 potential factors were analyzed through a hierarchical analytical model to identify key customers. Then, a linear regression model was used to develop a five-year program suitable for the client. Subsequently, a random forest model was used to verify that the program met the client's needs, and model training was used to predict the impact and probability of success of the program. Finally, in conjunction with the AHP model, the program was confirmed to significantly reduce illegal trade. This study proposes an integrated approach that provides an effective solution for wildlife conservation, and hopefully will lead to a win-win situation for the protection of ecological balance and human interests.
References
- Margulies J D, Bullough L A, Hinsley A, et al. Illegal wildlife trade and the persistence of “plant blindness” [J]. Plants, People, Planet, 2019, 1(3): 173-182.
- Subramanian N, Ramanathan R. A review of applications of Analytic Hierarchy Process in operations management[J]. International Journal of Production Economics, 2012, 138(2): 215-241.
- Wang L. Performance Evaluation and Management Method of Music Tutoring Schools Based on Analytic Hierarchy Process[J]. Discrete Dynamics in Nature and Society, 2022, 2022.
- Aslam M. Introducing Kolmogorov–Smirnov tests under uncertainty: an application to radioactive data[J]. ACS omega, 2019, 5(1): 914-917.
- Maulud D, Abdulazeez A M. A review on linear regression comprehensive in machine learning[J]. Journal of Applied Science and Technology Trends, 2020, 1(2): 140-147.
- Denisko D, Hoffman M M. Classification and interaction in random forests[J]. Proceedings of the National Academy of Sciences, 2018, 115(8): 1690-1692.