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Research on Forecasting Strategy of Environmental Data Based on Neural Network

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DOI: 10.23977/csic.2018.0958

Author(s)

Ying Chen, Han Zheng and Fengyu Yang

Corresponding Author

Ying Chen

ABSTRACT

In this paper, NO2, the most common polluting gas in the air, is selected as experimental subject, and an improved prediction algorithm is proposed. First, the gas data is preprocessed and optimized by Chauville's method, so that the gas data become more reliable. Then the improved prediction network model is created, and the input data is input into the network model to predict the results. Finally, the network model is established through multiple random sampling learning in large samples. The simulation results show that the improved neural network-based data prediction algorithm is superior to the original method in term of accuracy and stability.

KEYWORDS

Polluted gas, Data forecast, Neural Networks

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