Mathematical analysis of a predator-prey system with shared resource, climatic effects, and neural network insight

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Imran Abbas*
Asad Ejaz
Syed Saqib Shah

Abstract

This research paper introduces a predator-prey system in which both organisms depend on a common sustenance source. In order to establish environmental dynamics that are more plausible, we integrated climatic effects on the predator population by implementing a sigmoidal function. The objective is to study the impact of climate on the population dynamics of interacting species by employing mathematical tools like stability analysis and Artificial Neural Networks. By employing meticulous mathematical analysis, we were able to ascertain the equilibrium points of the system and examine their stability on a global scale. Our investigation covered both diffusive and non-diffusive models, providing insight into the unique dynamical characteristics of each. Moreover, in order to leverage the capabilities of modern computational methods, a neural network strategy was implemented to analyses the system's complexities in greater detail. In conclusion, exhaustive diagrams were used to meticulously illustrate the effect of varying parameters, thereby providing invaluable insights into the behavior of the system under various conditions.

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Article Details

Abbas, I., Ejaz, A., & Shah, S. S. (2024). Mathematical analysis of a predator-prey system with shared resource, climatic effects, and neural network insight. Annals of Mathematics and Physics, 7(2), 162–173. https://doi.org/10.17352/amp.000120
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Copyright (c) 2024 Abbas I, et al.

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Effati S, Mansoori A, Eshaghnezhad M, An efficient projection neural network for solving bilinear programming problems, Neurocomputing. 2015; 168:1188–1197.

Holling CS, The functional response of invertebrate predators to prey density, Mem. Entomol. Soc. Can. 1966; 98 (S48):5–86.

Biazar J, Montazeri R, A computational method for solution of the prey and predator problem, Appl. Math. Comput. 2005; 163 (2):841–847.

Solis FJ, Self-limitation in a discrete predator–prey model, Math. Comput. Modelling. 2008; 48(1):191–196.

Danca M, Codreanu S, Bakó B. Detailed analysis of a nonlinear prey-predator model. J Biol Phys. 1997 Mar; 23(1):11-20. doi: 10.1023/A:1004918920121. PMID: 23345647; PMCID: PMC3456267.

Jing Z, Yang J. Bifurcation and chaos in discrete-time predator–prey system, Chaos Solitons Fractals. 2006; 27(1): 259–277.

Liu X, Xiao D. Complex dynamic behaviors of a discrete-time predator–prey system, Chaos Solitons Fractals. 2007; 32(1):80–94.

Elsadany AEA, El-Metwally HA, Elabbasy EM, Agiza HN. Chaos and bifurcation of a nonlinear discrete prey-predator system, Comput. Ecol. Softw. 2012; 2(3):169.

Summers D, Cranford JG, Healey BP. Chaos in periodically forced discrete-time ecosystem models, Chaos Solitons Fractals. 2000; 11(14): 2331–2342.

Paul S, Mondal SP, Bhattacharya P, Numerical solution of Lotka Volterra prey predator model by using Runge–Kutta–Fehlberg method and Laplace Adomian decomposition method, Alex. Eng. J. 2016; 55(1):613–617.

Batiha B. The solution of the prey and predator problem by differential transformation method, Int. J. Basic Appl. Sci. 2014; 4(1):36–43.

Garvie MR, Burkardt J, Morgan J. Simple finite element methods for approximating predator-prey dynamics in two dimensions using MATLAB. Bull Math Biol. 2015 Mar; 77(3):548-78. doi: 10.1007/s11538-015-0062-z. Epub 2015 Jan 24. PMID: 25616741.

Bildik N, Deniz S. The use of Sumudu decomposition method for solving predator-prey systems, Math. Sci. Lett. 2016; 5(3):285–289.

Yu J, Yu J. Homotopy analysis method for a prey-predator system with Holling IV functional response, Appl. Mech. Mater. 2014.

Ray SS. A new coupled fractional reduced differential transform method for the numerical solution of fractional predator-prey system, CMES Comput. Model. Eng. Sci. 2015; 105(3).

Fang L, Wang J. The global stability and pattern formations of a predator–prey system with consuming resource. Applied Mathematics Letters. 2016; 58:49-55.

Ejaz A, Nawaz Y, Arif MS, Mashat DS, Abodayeh K. Stability Analysis of Predator-Prey System with Consuming Resource and Disease in Predator Species. CMES-Computer Modeling in Engineering & Sciences. 2022; 132(2).

Ejaz A, Nawaz Y, Arif MS, Mashat DS, Abodayeh K. Stability Analysis of Predator-Prey System with Consuming Resource and Disease in Predator Species. CMES-Computer Modeling in Engineering & Sciences. 2022; 132(2).

Arif MS, Abodayeh K, Ejaz A. Stability Analysis of Fractional-Order Predator-Prey System with Consuming Food Resource. Axioms. 2023; 12(1):64.

Mall S, Chakraverty S. Chebyshev Neural Network based model for solving Lane–Emden type equations, Appl. Math. Comput. 2014; 247:100–114.

Chakraverty S, Mall S. Regression-based weight generation algorithm in neural network for solution of initial and boundary value problems, Neural Comput. Appl. 2014; 25(3–4):585–594.

Raja MAZ, Samar R, Alaidarous ES, Shivanian E. Bio-inspired computing platform for reliable solution of Bratu-type equations arising in the modeling of electrically conducting solids. Appl Math Model. 2016; 40(11):5964-5977.

Raja MA, Zameer A, Khan AU, Wazwaz AM. A new numerical approach to solve Thomas-Fermi model of an atom using bio-inspired heuristics integrated with sequential quadratic programming. Springerplus. 2016 Aug 23; 5(1):1400. doi: 10.1186/s40064-016-3093-5. PMID: 27610319; PMCID: PMC4994819.

Effati S, Pakdaman M. Artificial neural network approach for solving fuzzy differential equations. Inform Sci. 2010; 180(8):1434-1457.

Raja MAZ. Solution of the one-dimensional Bratu equation arising in the fuel ignition model using ANN optimised with PSO and SQP. Connect Sci. 2014; 26(3):195-214.

Baymani M, Effati S, Niazmand H, Kerayechian A. Artificial neural network method for solving the Navier–Stokes equations. Neural Comput Appl. 2015; 26(4):765-773.

Raja MAZ, Ahmad I, Khan I, Syam MI, Wazwaz AM. Neuro-heuristic computational intelligence for solving nonlinear pantograph systems. Front Inf Technol Electron Eng. 2017; 18(4):464-484.

Effati S, Skandari MHN. Optimal control approach for solving linear Volterra integral equations. Int J Intell Syst Appl. 2012; 4(4):40.

Jafarian A, Measoomy S, Abbasbandy S. Artificial neural networks based modeling for solving Volterra integral equations system. Appl Soft Comput. 2015; 27:391-398.

Raja MAZ, Farooq U, Chaudhary NI, Wazwaz AM. Stochastic numerical solver for nanofluidic problems containing multi-walled carbon nanotubes. Appl Soft Comput. 2016; 38:561-586.

Effati S, Buzhabadi R. A neural network approach for solving Fredholm integral equations of the second kind. Neural Comput Appl. 2012; 21(5):843-852.

Ahmad I, Raja MAZ, Bilal M, Ashraf F. Neural network methods to solve the Lane–Emden type equations arising in thermodynamic studies of the spherical gas cloud model. Neural Comput Appl. 2017; 28(1):929-944. Available from: http://dx.doi.org/10.1007/s00521-016-2400-y

Raja MAZ, Khan JA, Chaudhary NI, Shivanian E. Reliable numerical treatment of nonlinear singular Flierl–Petviashivili equations for unbounded domain using ANN, GAs, and SQP. Appl Soft Comput. 2016; 38:617-636.

Sabouri J, Effati S, Pakdaman M. A neural network approach for solving a class of fractional optimal control problems. Neural Process Lett. 2016; (1-16).

Effati S, Mansoori A, Eshaghnezhad M. An efficient projection neural network for solving bilinear programming problems. Neurocomputing. 2015; 168:1188-1197.

Raja MAZ, Khan MAR, Mahmood T, Farooq U, Chaudhary NI. Design of bio-inspired computing technique for nanofluidics based on nonlinear Jeffery–Hamel flow equations. Can J Phys. 2016; 94(5):474-489.

Kumar M, Yadav N. Numerical solution of Bratu’s problem using multilayer perceptron neural network method. Nat Acad Sci Lett. 2015; 38(5):425-428.