Neural Network and Multiple Regression Models for PM₁₀ Prediction in Athens: A Comparative Assessment

TLDR

Particulate atmospheric pollution in urban areas significantly impacts human health, making accurate predictions of ambient concentrations essential for public awareness and air quality management. The study investigates using neural network methods to forecast daily average PM10 concentrations as an alternative to conventional statistical models. Using a two‑year dataset from a central Athens site and meteorological inputs, the authors developed and evaluated neural network and multiple linear regression models. Neural network models outperformed regression models, achieving 8.2–9.4% lower RMSE and 7–13% lower false‑alarm rates, demonstrating that properly trained ANNs can adequately meet particulate pollution forecasting needs.

Abstract

Abstract Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.