Predicting Particulate Matter (PM_(2.5)) Concentrations in the Air of Shahr-e Ray City, Iran, by Using an Artificial Neural Network

摘要

We applied MLP and RBF neural networks to predict PM_(2.5) concentration levels in hourly time series based on the air pollutant database compiled from actual monitoring conducted by the AQCC in Shahr-e Ray City in Iran. A summary of the conclusions is as follows: 1. In both the MLP and RBF models, we achieved acceptable coefficients of determination, which were 0.954 and 0.998, respectively.