Research on air pollution prediction model based on convolutional neural networks
基于卷积神经网络的大气污染预测模型研究

摘要

随着工业化和城市化的加速发展，大气污染已成为全球亟需解决的环境问题之一。细颗粒物（PM2.5）作为主要的空气污染物，对人类健康、生态系统和气候变化产生了深远的影响。本研究旨在探索卷积神经网络（CNN）这一深度学习技术在PM2.5浓度预测中的应用潜力，通过构建高效、精准的预测模型，为大气污染防控提供科学依据和技术支持[1]。在模型构建阶段，本研究创新性地将卷积神经网络（CNN）引入大气污染预测领域，提出了一种结合时间序列与空间数据的CNN预测模型。该模型利用卷积层自动提取数据的局部特征与空间依赖关系，通过池化层减少数据维度，降低计算复杂度，并通过全连接层完成PM2.5浓度的预测任务。

Abstract

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.