深度学习在PM2.5预测中的应用

Application of Deep Learning in PM2.5 Prediction

近年来,随着我国环境保护措施的不断加强,如何有效控制大气污染也越来越重要,而PM2.5作为污染物的重要来源也提上日程,因此对PM2.5做有效的预测具有重要的现实意义和社会价值。收集2015年1月1日到2019年3月10日昆明市空气质量指数信息,首先利用传统的时间序列模型预测PM2.5,接下来设计LSTM循环神经网络PM2.5预测模型。实验结果表明:与传统的时间序列序列相比,LSTM模型预测精度更高,具有较强的实用性。

In recent years, with the continuous strengthening of China's environmental protection measures, how to effectively control air pollution is becoming more and more important, and PM2.5 as an important source of pollutants has also been put on the agenda, so the effective prediction of PM2.5 has Important practical and social values. Collects information on Kunming air quality index from January 1, 2015 to March 10, 2019. First, uses the traditional time series model to predict PM2.5. Next, designs the LSTM cyclic neural network PM2.5 prediction model. The experimental results show that the LSTM model has higher prediction accuracy than the traditional time series, has a strong practicality.