Uncertainty propagation refers to the process of identifying, quantifying, and analyzing how uncertainties in input parameters or variables propagate through a system or model to affect the uncertainty in the output or results. This research area is critical in fields such as engineering, economics, and environmental science, where understanding and managing uncertainties is essential for making informed decisions. Various techniques, such as Monte Carlo simulations, sensitivity analyses, and Bayesian statistics, are commonly used in uncertainty propagation studies to assess the impact of uncertainties on model predictions or outcomes. The goal of uncertainty propagation research is to improve the reliability and robustness of models and decision-making processes in the face of inevitable uncertainties.