A conceptual framework for developing digital twins of human-environmental systems

Human-environmental systems (HES) are incredibly intricate and interlinked structures.They are influenced by a wide range of variables that can affect each other in countless ways, which makes them challenging to study, comprehend, and forecast.This research presents a ground-breaking approach to developing digital twins for HES using artificial intelligence.A digital twin is a virtual replica of a physical entity or system, which can be manipulated and tested in various ways to better understand and predict the behaviour of its real-world counterpart.This innovative digital framework untangles complicated correlations, enhances predictive capabilities, and aids in making decisions that are environmentally sustainable.The proposed framework goes beyond traditional system models by creating a digital duplicate of the HES that accurately reflects the dynamic behaviour of the system in the real world.The key innovation lies in the use of machine learning algorithms and causal inference methods.These are integrated to allow for detailed simulations, accurate predictions, and the provision of decision support.