%0 Journal Article
%T 有关空气质量预报问题分析与建模<br>Analysis and Modeling of Air Quality Forecasting Issues
%A 吴娟娟
%J Pure Mathematics
%P 489-508
%@ 2160-7605
%D 2024
%I Hans Publishing
%R 10.12677/pm.2024.145203
%X 空气质量等级综合评价对人们的生产活动有着重要意义。各监测点数据由于包含了连续而丰富的各种气象条件数据，因此能成为空气质量估计与预测的重要手段。本文通过建立多元线性回归方程描述了污染物浓度与气象实测数据的关系；利用BP神经网络深度学习模型来估计各污染物浓度以及进一步可以计算AQI值；建立模型预测空气中五种主要污染物浓度，经过由深及浅、循序渐进的分析，能较为准确地预测空气质量，为人们生产生活提供参考。<br />
The comprehensive evaluation of air quality levels holds significant importance for people’s productive activities. The data from various monitoring points, encompassing continuous and rich meteorological conditions, serve as vital means for air quality estimation and prediction. This paper describes the relationship between pollutant concentrations and meteorological observation data by establishing a multiple linear regression equation. Additionally, it utilizes a BP neural network deep learning model to estimate pollutant concentrations and further calculate AQI values. By establishing a model to predict the concentrations of five major pollutants in the air, through a systematic and progressive analysis, it can accurately forecast air quality, providing a reference for people’s production and life.
%K 多元线性回归，BP神经网络，深度学习，均方误差<br>Multiple Linear Regression
%K BP Neural Network
%K Deep Learning
%K Mean Squared Error
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=88720