基于机器学习的上海市空气质量预测方法研究  

Prediction Method Research on Air Quality in Shanghai Based on Machine Learning

作　　者：张勤[1] 郭进利[1] ZHANG Qin;GUO Jin-li(Bussiness School,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《软件导刊》2022年第8期33-38,共6页Software Guide

基　　金：国家自然科学基金项目(71571119)。

摘　　要：空气质量与气象因子之间存在较强的非线性关系且多数学者仅基于单一方法对该问题进行研究和改进,导致空气质量预测精度不佳。为更好地预测上海市空气质量,选取2016-2021年上海市空气质量数据,分别使用BP神经网络、决策树和支持向量机算法构建空气质量预测模型对次日空气质量等级进行预测。研究结果表明:①支持向量机的预测精度最高,CART决策树次之,BP神经网络预测效果最差;②在4类基于不同核函数和分类方法的支持向量机模型中,基于线性核函数和一对多分类方法的支持向量机预测准确率最高,为80%;③当空气质量为良时,预测值和真实值的重合度高。将机器学习方法应用于空气质量预报合理有效,可为市民出行提供参考建议。Because there was a strong nonlinear relationship between air quality and meteorological factors and many scholars only studied this problem based on a single method,select the air quality data of Shanghai from 2016 to 2021,and then built an air quality prediction model using BP neural network,decision tree and SVM to predict the air quality grade of the next day.The results show that:(1)The model based on SVM has the highest prediction accuracy,cart decision is the second,and BP neural network is the worst;(2)Among the four kinds of SVM models based on different kernel functions and classification methods,the prediction accuracy of SVM based on linear kernel function and one-against-the rest method is the highest,which is 80%;(3)When the air quality is good,the coincidence between the predicted value and the real value is high.Applying machine learning method to air quality prediction is reasonable and can provide suggestions for citizens′travel.

关 键 词：机器学习 BP神经网络 支持向量机 决策树 空气质量 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]