Sharif Digital Repository

This research aims to evaluate the capability of a physics-based machine learning model in forecasting PM₁₀ concentration (wich serves as dust particle concentration). Previous studies have primarily employed two methodological approaches for dust concentration prediction namely one based on the physical dynamics governing dust events and the other relying on data-driven techniques through machine learning. In this study, a Physics-Informed Neural Network (PINN) model was employed, integrating the strengths of both approaches. Training the PINN model required gridded meteorological inputs and corresponding gridded PM₁₀ data. To ensure reliability, correlation analyses were conducted between...