TY - GEN
T1 - Air quality forecasting using SVR with quasi-linear kernel
AU - Zhu, Huilin
AU - Hu, Jinglu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Air pollution has threaten people's health. It is urgent for the government to strengthen and enhance air pollution monitoring capacity. In this paper, we propose an air quality prediction model to infer air pollutant concentrations, such as CO, NOx, NO2. The idea is to design a sophisticate piecewise linear model by using a gated linear network. A top k% winner-take-all autoencoder is first built to generate a set of binary sequences as the gate control signals, so as to perform the input space partitioning. The piecewise linear model is then identified in an exact same way as a standard support vector regression (SVR) with a quasi-linear kernel composed by using the gate control signals. Results of our experiments shows that our proposed SVR prediction model outperforms other state-of-the-art methods.
AB - Air pollution has threaten people's health. It is urgent for the government to strengthen and enhance air pollution monitoring capacity. In this paper, we propose an air quality prediction model to infer air pollutant concentrations, such as CO, NOx, NO2. The idea is to design a sophisticate piecewise linear model by using a gated linear network. A top k% winner-take-all autoencoder is first built to generate a set of binary sequences as the gate control signals, so as to perform the input space partitioning. The piecewise linear model is then identified in an exact same way as a standard support vector regression (SVR) with a quasi-linear kernel composed by using the gate control signals. Results of our experiments shows that our proposed SVR prediction model outperforms other state-of-the-art methods.
KW - Air pollutant concentrations
KW - Quasi-linear kernel
KW - Support vector regression
KW - Winner-take-all autoencoder
UR - http://www.scopus.com/inward/record.url?scp=85074157809&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074157809&partnerID=8YFLogxK
U2 - 10.1109/CITS.2019.8862114
DO - 10.1109/CITS.2019.8862114
M3 - Conference contribution
AN - SCOPUS:85074157809
T3 - CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems
BT - CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems
A2 - Obaidat, Mohammad S.
A2 - Mi, Zhenqiang
A2 - Hsiao, Kuei-Fang
A2 - Nicopolitidis, Petros
A2 - Cascado-Caballero, Daniel
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019
Y2 - 28 August 2019 through 31 August 2019
ER -