Affiliations: [a] Department of Energy and Resources Engineering, College of Engineering, Peking University, Beijing, China
| [b] Beijing Key Laboratory for Solid Waste Utilization and Management, Peking University, Beijing, China
Correspondence:
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Corresponding author. Xidong Wang, Department of Energy and Resources Engineering, College of Engineering, Peking University, Beijing, China; Beijing Key Laboratory for Solid Waste Utilization and Management, Peking University, Beijing, China. E-mail: wang_xidong@126.com.
Abstract: This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.
Keywords: Neural network, machine learning, air pollution, intelligent monitoring
