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Neural Network Modeling of Vehicle Gross Emitter Prediction Based on Remote Sensing Data

Vehicle emissions are a significant source of air pollution in cities. A neural network model for vehicle gross emitter prediction was established based on remote sensing data. The states of vehicle emission remote sensing system in China were described first, followed by a brief introduction to idle testing and remote sensing testing. After data collection, the choices in the algorithm and architecture, as well as original data were then analyzed and compared. The back-propagation (BP) neural network model with 7-20-1 architecture was also selected as the optimal approach with satisfied prediction. Compared with traditional model, the proposed approach has better accuracy and generality. The 81.63% correct results show the potentiality and validity of remote sensing for gross emitter prediction by using the neural network