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基于多元回归的延安市PM2.5浓度预测

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工业化进程的加快带来的不只是经济的飞速发展,还有以PM2.5为主的污染物浓度的增加,给人类的健康以及环境的治理带来不利影响,合理有效的PM2.5浓度预测对于人类健康和环境治理有着重要意义.文中设计了基于多元回归模型的PM2.5浓度预测模型,分别预测了延安市春季、夏季、秋季和冬季的PM2.5浓度,与极限树回归、Catboost回归和K邻近回归等回归模型的预测结果进行对比.结果表明多元回归模型的误差较小,拟合精度较高,为延安市大气污染的治理提供了可靠的科学依据.
Prediction of PM2.5 concentration in Yan'an City based on multiple regression
The acceleration of industrialization not only brings rapid economic development,but also increa-ses the concentration of pollutants mainly PM2.5,which brings adverse effects on human health and envi-ronmental governance.Reasonable and effective prediction of PM2.5 concentration is of great significance to human health and environmental governance.In this paper,a prediction model of PM2.5 concentration based on multiple regression model is designed to predict the PM2.5 concentration of Yan'an city in spring,summer,autumn and winter respectively,and compared with the prediction results of limit tree re-gression,Catboost regression and K proximity regression.The results show that the error of multiple regres-sion model is the smallest and the fitting precision is the highest,which provides a reliable scientific basis for air pollution control in Yan'an city.

Kev words:multiple regressionPM2.5 predictionextra tree regressionrandom forest

王思源、任瑛、夏必胜、王文发

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延安大学数学与计算机科学学院,陕西延安 716000

多元回归 PM2.5预测 极限树回归 随机森林

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金陕西省自然科学基础研究计划延安大学博士科研启动基金延安市科技专项延安市科技局项目延安大学校级项目

61866038617630466196205961902339620412122021JM-418YD-BK2019-06YDFK073203010096205040306

2024

10.13274/j.cnki.hdzj.2024.05.003

信息技术
黑龙江省信息技术学会 中国电子信息产业发展研究院　中国信息产业部电子信息中心

信息技术

CSTPCD
影响因子：0.413
ISSN：1009-2552
年,卷(期)：2024.(5)