Beijing PM2.5 Influencing Factors Analysis Based on GAM

被引：2

作者：

Tao, Guiping ^{[1
]}

Chen, Hongmei ^{[1
]}

Li, Wenjun ^{[2
]}

机构：

[1] Capital Univ Econ & Business, Sch Stat, Beijing, Peoples R China

[2] Beijing Normal Univ, Collaborat Innovat Ctr Assessment Basic Educ Qual, Beijing, Peoples R China

来源：

2020 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2020) | 2020年

关键词：

PM2.5; GAM; Predict;

D O I：

10.1109/WIIAT50758.2020.00139

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Haze has become an unavoidable problem in many cities with economic growth, and its influencing factors are complex. This paper selects ground-level PM2.5 concentrations as an indicator to measure haze. Considering nonlinear factors, this paper applies GAM model to analyze relationship between PM2.5 concentrations in Beijing and its influencing factors, including PM2.5 precursors, meteorological factors, motor vehicle factors and holiday factors, and then makes predictions for PM2.5 concentrations by using the established model for the future five days. Both the fitting effect and the predict effect have been proved very effective. This study is helpful to control haze and improve air quality.

引用

页码：916 / 921

页数：6

共 9 条

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