Enhancing Predator-Prey Models with Machine Learning Techniques: A Comparative Study

This study explores the integration of machine learning techniques with traditional predator-prey models to enhance their predictive accuracy and applicability in complex ecological systems. We compare the performance of classical Lotka-Volterra equations with machine learning-enhanced models, including artificial neural networks (ANNs) and support vector machines (SVMs). Using both simulated and real-world data from a wolf-moose ecosystem, we demonstrate that machine learning approaches can significantly improve the accuracy of population dynamics predictions, especially in scenarios with multiple environmental variables. Our results show that ANN-enhanced models outperform traditional methods by 15-20% in predicting population fluctuations over extended periods. This research contributes to the growing field of computational ecology and offers new tools for wildlife management and conservation efforts.