Scientific Development Research

With the acceleration of industrialization and urbanization, air pollution has become one of the environmental problems to be solved in the world. Fine particulate matter (PM2.5), as a major air pollutant, has had a profound impact on human health, ecosystems and climate change. The purpose of this study is to explore the application potential of convolutional neural network (CNN), a deep learning technology, in PM2.5 concentration prediction, and to provide scientific basis and technical support for the prevention and control of air pollution by building an efficient and accurate prediction model. In the model construction stage, this study innovatively introduced convolutional neural network (CNN) into the field of air pollution prediction, and proposed a CNN prediction model combining time series and spatial data. The model uses the convolutional layer to automatically extract the local features and spatial dependence of the data, reduces the data dimension, reduces the computational complexity, and completes the prediction task of PM2.5 concentration through the fully connected layer.

Air pollution prediction; Convolutional neural network (CNN); PM2.5 concentration; Deep learning