Prediction of Airborne Particulate Matter PM 10 Based on Main Curve

DOI

10.2991/emim-17.2017.38How to use a DOI?

Keywords

The principal curve; Suspended particulate matter PM10; Imbalance; Threshold; Model

Abstract

It's very practical significance to predict the density of hazardous substance(such as PM10) in the air, However, in most cases, this kind of data have the characteristics of imbalance and sequential arrived online, It's difficult to realize rapid and effective prediction by traditional supervised learning methods. In order to solve this problem, a PM10 prediction method which based on the principal curve, build a PM10 model of PM10 from 2010 to 2012, received corresponding parameters by fitting, Finally, the main curve is obtained. corresponding threshold values of different density of PM10 respectively by a lot of experiment. The results show that the PM10 prediction model based on the principal curve predicts rapidly and a lower prediction error, meanwhile, the network structure is more compact.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - LiYun Wang
PY  - 2017/04
DA  - 2017/04
TI  - Prediction of Airborne Particulate Matter PM 10 Based on Main Curve
BT  - Proceedings of the 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)
PB  - Atlantis Press
SP  - 177
EP  - 181
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-17.2017.38
DO  - 10.2991/emim-17.2017.38
ID  - Wang2017/04
ER  -