Analysis of a Spatio-temporal Harvested Prey-Predator Model and a Machine Learning Framework for Prediction of Pattern Regimes with Demonstration on the Model
This paper investigates the spatio-temporal dynamics of a harvested prey-predator model with Holling Type-II functional response through analytical stability and bifurcation analysis, while simultaneously developing and validating a hybrid CNN-Random Forest machine learning framework to predict pattern regimes and construct transition diagrams across a two-parameter space.