Prediction of respirable suspended particulate level in Hong Kong downtown area using principal component analysis and artificial neural networks

Weizhen Lu; Huiyuan Fan; Wenjian Wang; Siu-Mong Lo; Andrew Y.T. Leung

Prediction of respirable suspended particulate level in Hong Kong downtown area using principal component analysis and artificial neural networks

Weizhen Lu
, Huiyuan Fan
, Wenjian Wang
, Siu-Mong Lo
, Andrew Y.T. Leung

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

Modeling of the pollutants' concentrations comprises an important part in the field of atmospheric environment research. Neural network modeling is regarded as a reliable and cost-effective method to achieve such prediction task. In this paper, the principal component analysis (PCA) technique is used to reduce and orthogonalize input variables of neural network (NN) model, which is established for forecasting the pollutants' concentrations in downtown area of Hong Kong. The new approach is demonstrated and validated with two practical cases of predicting the respirable suspended particulate (RSP) levels in central area in Hong Kong. The simulation results show that the proposed method is feasible and efficient.

Original language	English
Title of host publication	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages	49-53
Volume	1
Publication status	Published - 2002
Event	Proceedings of the 4th World Congress on Intelligent Control and Automation - Shanghai, China Duration: 10 Jun 2002 → 14 Jun 2002

Publication series

Name
Volume	1

Conference

Conference	Proceedings of the 4th World Congress on Intelligent Control and Automation
Place	China
City	Shanghai
Period	10/06/02 → 14/06/02

Research Keywords

Environmental
Modeling
Neural networks
Principal component analysis
Respirable suspended particulate

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@inproceedings{6d5deac65954452d9794e35934abe8b5,

title = "Prediction of respirable suspended particulate level in Hong Kong downtown area using principal component analysis and artificial neural networks",

abstract = "Modeling of the pollutants' concentrations comprises an important part in the field of atmospheric environment research. Neural network modeling is regarded as a reliable and cost-effective method to achieve such prediction task. In this paper, the principal component analysis (PCA) technique is used to reduce and orthogonalize input variables of neural network (NN) model, which is established for forecasting the pollutants' concentrations in downtown area of Hong Kong. The new approach is demonstrated and validated with two practical cases of predicting the respirable suspended particulate (RSP) levels in central area in Hong Kong. The simulation results show that the proposed method is feasible and efficient.",

keywords = "Environmental, Modeling, Neural networks, Principal component analysis, Respirable suspended particulate",

author = "Weizhen Lu and Huiyuan Fan and Wenjian Wang and Siu-Mong Lo and Leung, \{Andrew Y.T.\}",

year = "2002",

language = "English",

volume = "1",

pages = "49--53",

booktitle = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

note = "Proceedings of the 4th World Congress on Intelligent Control and Automation ; Conference date: 10-06-2002 Through 14-06-2002",

}

Lu, W, Fan, H, Wang, W, Lo, S-M & Leung, AYT 2002, Prediction of respirable suspended particulate level in Hong Kong downtown area using principal component analysis and artificial neural networks. in Proceedings of the World Congress on Intelligent Control and Automation (WCICA). vol. 1, pp. 49-53, Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, China, 10/06/02.

Prediction of respirable suspended particulate level in Hong Kong downtown area using principal component analysis and artificial neural networks. / Lu, Weizhen; Fan, Huiyuan; Wang, Wenjian et al.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA). Vol. 1 2002. p. 49-53.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Prediction of respirable suspended particulate level in Hong Kong downtown area using principal component analysis and artificial neural networks

AU - Lu, Weizhen

AU - Fan, Huiyuan

AU - Wang, Wenjian

AU - Lo, Siu-Mong

AU - Leung, Andrew Y.T.

PY - 2002

Y1 - 2002

N2 - Modeling of the pollutants' concentrations comprises an important part in the field of atmospheric environment research. Neural network modeling is regarded as a reliable and cost-effective method to achieve such prediction task. In this paper, the principal component analysis (PCA) technique is used to reduce and orthogonalize input variables of neural network (NN) model, which is established for forecasting the pollutants' concentrations in downtown area of Hong Kong. The new approach is demonstrated and validated with two practical cases of predicting the respirable suspended particulate (RSP) levels in central area in Hong Kong. The simulation results show that the proposed method is feasible and efficient.

AB - Modeling of the pollutants' concentrations comprises an important part in the field of atmospheric environment research. Neural network modeling is regarded as a reliable and cost-effective method to achieve such prediction task. In this paper, the principal component analysis (PCA) technique is used to reduce and orthogonalize input variables of neural network (NN) model, which is established for forecasting the pollutants' concentrations in downtown area of Hong Kong. The new approach is demonstrated and validated with two practical cases of predicting the respirable suspended particulate (RSP) levels in central area in Hong Kong. The simulation results show that the proposed method is feasible and efficient.

KW - Environmental

KW - Modeling

KW - Neural networks

KW - Principal component analysis

KW - Respirable suspended particulate

UR - https://www.scopus.com/pages/publications/0036955340

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0036955340&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

VL - 1

SP - 49

EP - 53

BT - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

T2 - Proceedings of the 4th World Congress on Intelligent Control and Automation

Y2 - 10 June 2002 through 14 June 2002

ER -

Prediction of respirable suspended particulate level in Hong Kong downtown area using principal component analysis and artificial neural networks

Abstract

Publication series

Conference

Research Keywords

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