Predator Prey Scavenger Model using Holling's Functional Response of Type III and Physics-Informed Deep Neural Networks

TL;DR

This paper introduces a predator-prey-scavenger model incorporating Holling's Type III response, utilizing physics-informed neural networks for parameter estimation and stability analysis based on real-world forest data.

Contribution

It develops a novel predator-prey-scavenger model with Holling's Type III response and applies physics-informed neural networks for accurate parameter estimation from real data.

Findings

Model shows satisfactory results in simulations

Parameters estimated from forest data are stable

Neural network approach prevents training issues

Abstract

Nonlinear mathematical models introduce the relation between various physical and biological interactions present in nature. One of the most famous models is the Lotka-Volterra model which defined the interaction between predator and prey species present in nature. However, predators, scavengers, and prey populations coexist in a natural system where scavengers can additionally rely on the dead bodies of predators present in the system. Keeping this in mind, the formulation and simulation of the predator prey scavenger model is introduced in this paper. For the predation response, respective prey species are assumed to have Holling's functional response of type III. The proposed model is tested for various simulations and is found to be showing satisfactory results in different scenarios. After simulations, the American forest dataset is taken for parameter estimation which imitates the real-world case. For parameter estimation, a physics-informed deep neural network is used with the Adam backpropagation method which prevents the avalanche effect in trainable parameters updation. For neural networks, mean square error and physics-informed informed error are considered. After the neural network, the hence-found parameters are fine-tuned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Finally, the hence-found parameters using a natural dataset are tested for stability using Jacobian stability analysis. Future research work includes minimization of error induced by parameters, bifurcation analysis, and sensitivity analysis of the parameters.