An accurate and low-cost PM2.5 estimation method based on Artificial Neural Network

PM_2.5 has already been a major pollutant in many cities in China. It is a kind of harmful pollutant which may cause several kinds of lung diseases. However, the existing methods to monitor PM_2.5 with high accuracy are too expensive to popularize. The high cost also limits the further researches about PM_2.5. This paper implements a method to estimate PM_2.5 with low cost and high accuracy by Artificial Neural Network (ANN) technique using other pollutants and meteorological factors that are easy to be monitored. An Entropy Maximization step is proposed to avoid the over-fitting related to the data distribution of pollutant data. Also, how to choose the input attributes is abstracted to an optimization problem. An iterative greedy algorithm is proposed to solve it, which reduces the cost and increases the estimation accuracy at the same time. The experiment shows that the linear correlation coefficient between the estimated value and real value is 0.9488. Our model can also classify PM_2.5 levels with a high accuracy. Additionally, the trade-off between accuracy and cost is investigated according to the price and error rate of each sensor.