Applying Linear and Nonlinear Models for the Estimation of Particulate Matter Variability
Tzanis, C. G., Alimissis, A., Philippopoulos, K., & Deligiorgi, D. (2019). Applying linear and nonlinear models for the estimation of particulate matter variability. Environmental Pollution, 246, 89–98.
This resource uses Inverse Distance Weighting, two linear regression models, the Multiple Linear Regression, and the Linear Mixed Model, along with a Feed Forward Neural Network (FFNN) model methods for the purpose of evaluating various methodologies used for spatial interpolation in the context of proposing an effective yet simple to apply scheme for PM spatial point estimations.