Digital Twins in Ecosystem Management: Combining Chemistry, Zoology, and Computational Models
Keywords:
Digital Twins, Digital Twins, Ecosystem Management, Ecosystem Management, Stochastic Differential Equations, Stochastic Differential Equations, Bifurcation Theory, Bifurcation Theory, Zoological Modeling, Zoological Modeling, Chemical Cycles, Chemical Cycles, Computational Ecology, Computational Ecology, Nonlinear Dynamics, Nonlinear Dynamics, Environmental Informatics, Environmental Informatics, Agent-Based Simulation, Agent-Based SimulationAbstract
Use of digital twin technology in the management of an ecosystem is a revolutionary development in the manner in which ecological phenomena are being observed, simulated and balanced. This paper presents a new research area in which chemistry, zoology, and digital modeling would be integrated by introducing digital twins in order to simulate the ongoing processes in a given environment in real-time. Using the theory of stochastic differential equation and bifurcation theory, a hybridised model is also presented in the paper and model fluctuations and intricate interspecies interaction with abiotic perturbation by chemical pollutants, temperature ranges, and hydrological patterns are simulated. The model is tested on in-the-wild data across the worlds of high- frequency climate perturbations in coastal marine ecosystems where there exist peak factors present in noise-supported transitions in population behavior and chemical cycles. In addition, we will show how including the concepts of large deviation theory and stochastic resonance into the study can help better predict critical ecosystem thresholds- usually called tipping points. The proposed computational framework of the study is an agent-based digital twins which dynamically update itself using live sensor information, providing self-updating and predictive properties based upon live, environmental signals. Results indicate there are huge improvements in excess of prediction accuracies of dissolved oxygen oscillations, species motion trends as well as biogeochemical loops when compared to conventional steady creatures. This method has the scalable promising of ecosystem resistance prediction, conservation planning, and regulatory laws in environmental policymaking. Application of domain research to chemistry and zoology in the context of a computational architecture establishes an effective interdisciplinary paradigm to the management of complex ecologies. By using mathematical modelling, simulation and aligning empirical information, this paper develops one of the tenets of future ecological informatics, digital twins.
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