Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission.
Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters.
Zeng
Jun
J
College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China. auzengjun@yahoo.com.cn
Guo
Hua-fang
HF
Hu
Yue-ming
YM
D000393:Air Pollutants / Q000032:analysis*; D001336:Automobiles; D002245:Carbon Dioxide / Q000032:analysis; D002248:Carbon Monoxide / Q000032:analysis; D002681:China; D004784:Environmental Monitoring; D006838:Hydrocarbons / Q000032:analysis; D016571:Neural Networks, Computer; D009589:Nitrogen Oxides / Q000032:analysis; D025341:Principal Component Analysis; D001335:Vehicle Emissions / Q000032:analysis*
D000393:Air Pollutants; D006838:Hydrocarbons; D009589:Nitrogen Oxides; D001335:Vehicle Emissions; D002245:Carbon Dioxide; D002248:Carbon Monoxide