摘要
使用机器学习算法对未来AQI进行预测,有助于从宏观角度分析未来空气质量变化趋势。在传统上使用单一的机器学习模型对空气质量进行预测时,很难在不同AQI波动趋势下都能获得较好的预测效果。为有效解决该问题,在预测方式上进行改进,针对使用随机森林模型和基于卷积神经网络和注意力机制的长短期记忆模型对成都市的AQI数据进行预测时,在不同的AQI波动趋势下两者的预测准确度不同的特点,设计了一种差异融合分析模型。实验结果表明,提出的差异融合分析模型的MSE误差较随机森林模型降低了5.8%,较基于卷积神经网络和注意力机制的长短期记忆模型降低了6.3%。
The prediction of AQI in the future by using machine learning algorithm is helpful to analyze the trend of air quality change in the future from a macro perspective. When a single machine learning model is traditionally used to predict air quality, it is difficult to obtain good prediction results under different AQI fluctuation trends. In order to effectively solve this problem, the prediction method is improved. When using random forest model and long and short-term memory model based on convolution neural network and attention mechanism to predict the AQI data in Chengdu, a difference fusion analysis model is designed according to the characteristics of different prediction accuracy under different AQI fluctuation trends. The experimental results show that the MSE of the proposed difference fusion analysis model is 5.8% lower than that of the random forest model, and 6.3% lower than that of the long-term and short-term memory model based on convolutional neural network and attention mechanism.
作者
高嵩
何卓骏
刘子岳
刘家明
王刚
李登柯
Gao Song;He Zhuojun;Liu Ziyue;Liu Jiaming;Wang Gang;Li Dengke(School of Mechanical and Electrical Engineering,Chengdu University of Technology.Chengdu 610059,China;Key Lab of Earth Exploration Information Techniques of Ministry of Education,Chengdu University of Technology,Chengdu 610059,China)
出处
《电子测量技术》
北大核心
2021年第18期85-92,共8页
Electronic Measurement Technology
基金
国家自然科学基金(41930112)项目资助。
关键词
空气质量指数
差异融合
随机森林
长短期记忆模型
支持向量机
air quality index
difference fusion
random forest
long short-term memory
support vector machines