%0 Journal Article
%T Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission
%A ZENG Jun
%A GUO Hua-fang
%A HU Yue-ming
%A <br>ZENG Jun
%A GUO Hua-fang
%A HU Yue-ming
%J 环境科学学报(英文版)
%D 2007
%I 
%X 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.
%K vehicle emission
%K remote sensing
%K neural network
%K principal component analysis
%K regression analysis
%K vehicle emission
%K remote sensing data
%K emitter
%K neural network model
%K comparison
%K validity
%K method of
%K identification
%K provided
%K research
%K result
%K principal component analysis
%K percentage of hits
%K established
%K optimal
%K approach
%K purpose
%K basis
%K selection
%K algorithm<br>车辆排放
%K 遥感数据
%K 出租汽车
%K 总排放物
%K 人工神经网络模型
%K 主成分分析
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=3FF3ABA7486768130C3FF830376F43B398E0C97F0FF2DD53&cid=A7CA601309F5FED03C078BCE383971DC&jid=6CB1530875F53489BF1E81BD87B7F5E6&aid=7D917DDE7DB790695F77E4A3E14CCB4B&yid=A732AF04DDA03BB3&vid=2A8D03AD8076A2E3&iid=E158A972A605785F&sid=CB3428B1EFB1C133&eid=6D6BFCF0101BC091&journal_id=1001-0742&journal_name=Journalofenvironmentalsciences(China)&referenced_num=1&reference_num=23