Open AccessDeep Convolutional Neural Network for Air Quality Prediction
Author(s) -
Yu-Shun Mao,
Shie-Jue Lee
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/3/032046
Subject(s) - convolutional neural network , computer science , deep learning , pooling , artificial intelligence , air quality index , convolution (computer science) , series (stratigraphy) , scalability , time series , machine learning , artificial neural network , pattern recognition (psychology) , data mining , meteorology , paleontology , physics , database , biology
In this paper, we tackle air quality forecasting by using deep learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter PM2.5 and sulfur dioxide). Deep learning (DL), as one of the most popular techniques, is able to efficiently train a scalable model on big data by optimization algorithms. The model is trained for air quality prediction with time series data. Our method takes the deep convolutional neural network (CNN) as the sequence module and inputs the time series data into the CNN model in turn for training. CNN is composed of many functional layers, such as convolution, pooling and ReLU. Convolution layer can effectively extract the sequential features of time series data. Sequential features work better than general features of time series data. Down-sampling in CNN is performed by the Pooling layer. Experimental results show that CNN performs well for air quality prediction.