Issue |
E3S Web Conf.
Volume 498, 2024
III International Conference on Actual Problems of the Energy Complex: Mining, Production, Transmission, Processing and Environmental Protection (ICAPE2024)
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Article Number | 02006 | |
Number of page(s) | 8 | |
Section | Ecology and Environmental Protection Issues | |
DOI | https://doi.org/10.1051/e3sconf/202449802006 | |
Published online | 06 March 2024 |
Model for forest ecosystems based on quantum optimization
1 Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University, 39, Kari Niyazov ave., Tashkent, 100000, Uzbekistan
2 Chirchik State Pedagogical University, 104, str. Amir Temur, Tashkent, 100000, Uzbekistan
* Corresponding author: dilnoz134@rambler.ru
This paper presents a mathematical model developed to describe the dynamics of forest ecosystems. The model is based on the principles of cross-diffusion, consider the interaction between two plant species in a forest environment. The model considers various parameters, like diffusion, growth and interaction coefficients and environmental capacities between species. Factors of influence of external conditions on each species of plants are also introduced. The differential equations are solved numerically using the finite difference method. This paper studies cross-diffusion dynamics by combining classical differential equations and quantum-inspired optimization techniques. The focus is on cross-diffusion processes, where populations interact through complex mechanisms of diffusion and reaction. The study uses a hybrid approach that combines classical methods for solving differential equations with quantum optimization, a quantum computing platform. Visualization of the results is presented in the form of 3D graphs reflecting the spatial distribution of plant populations in a forest ecosystem at various time steps. The resulting mathematical model and its visualization provide a tool for a deeper understanding of the influence of various factors on the dynamics of forest ecosystems. Analysis of such a model could be useful for predicting longterm changes in forests and developing sustainable forest management strategies.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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