Particulate Matter Concentration Estimation from Satellite Aerosol and Meteorological Parameters: Data-Driven Approaches

被引：14

作者：

Thi Nhat Thanh Nguyen ^{[1
]}

Viet Cuong Ta ^{[1
]}

Thanh Ha Le ^{[1
]}

Mantovani, Simone ^{[2
,3
]}

机构：

[1] Univ Engn & Technol, VNU, 144 XuanThuy, Hanoi, Vietnam

[2] MEEO Srl, I-44122 Ferrara, Italy

[3] SISTEMA GmbH, Vienna, Austria

来源：

KNOWLEDGE AND SYSTEMS ENGINEERING (KSE 2013), VOL 1 | 2014年 / 244卷

关键词：

AIR-QUALITY ASSESSMENT; CALIBRATION APPROACH; MODIS; POLLUTION; PM2.5;

D O I：

10.1007/978-3-319-02741-8_30

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Estimation of Particulate Matter concentration (PM1, PM2.5 and PM10) from aerosol product derived from satellite images and meteorological parameters brings a great advantage in air pollution monitoring since observation range is no longer limited around ground stations and estimation accuracy will be increased significantly. In this article, we investigate the application of Multiple Linear Regression (MLR) and Support Vector Regression (SVR) to make empirical data models for PM1/2.5/10 estimation from satellite-and ground-based data. Experiments, which are carried out on data recorded in two year over Hanoi - Vietnam, not only indicate a case study of regional modeling but also present comparison of performance between a widely used technique (MLR) and an advanced method (SVR).

引用

页码：351 / 362

页数：12

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