Optimizing wildlife protection through a Markov model and genetic algorithms: a long-term predictive study

Yuxi Qi; Mohan Ji; Pengfei Du; Liquan Pan

doi:10.1117/12.3004856

9 October 2023 Optimizing wildlife protection through a Markov model and genetic algorithms: a long-term predictive study

Yuxi Qi, Mohan Ji, Pengfei Du, Liquan Pan

Author Affiliations +

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910V (2023) https://doi.org/10.1117/12.3004856
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Wildlife protection is a critical issue in natural conservation, and it is necessary to establish a scientific model to provide more living places for wildlife while balancing human interests. In this paper, we propose a Markov state transition model to optimize the management mode of different areas in a wildlife reserve and use genetic algorithms to simulate future trends and compare management models. Our results suggest that the reserve should be divided into two areas, one for the survival of wild animals and the other for human visits, with the survival area covering more than 70% of the reserve's area. Our prediction model shows a 30% growth trend for wild animals in the survival area and a 20% change trend in the human visit area, indicating the effectiveness of our approach. Finally, we provide specific strategies to achieve a comprehensive and balanced approach to human-wildlife conflict.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuxi Qi, Mohan Ji, Pengfei Du, and Liquan Pan "Optimizing wildlife protection through a Markov model and genetic algorithms: a long-term predictive study", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910V (9 October 2023); https://doi.org/10.1117/12.3004856

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