Vol.58, No.1, 2019, pp.215-227, doi:10.32604/cmc.2019.03763
GA-BP Air Quality Evaluation Method Based on Fuzzy Theory
  • Ma Ning1,*, Jianhe Guan1, Pingzeng Liu2, Ziqing Zhang3, Gregory M. P. O’Hare4
China University of Geoscience, Beijing, 100083, China.
Shandong Agricultural University, Taian, 271018, China.
Shandong Vocational Institute of Fashion Technology, Taian, 271000, China.
University College Dublin (UCD), Belfield, Dublin, 999014, Ireland.
* Corresponding Author: Ma Ning. Email: .
With the rapid development of China’s economy, the scale of the city has been continuously expanding, industrial enterprises have been increasing, the discharge of multiple pollutants has reached the top of the world, and the environmental problems become more and more serious. The air pollution problem is particularly prominent. Air quality has become a daily concern for people. In order to control air pollution, it is necessary to grasp the air quality situation in an all-round way. It is necessary to evaluate air quality. Accurate results of air quality evaluation can help people know more about air quality. In this paper, refers to previous research results and different evaluation methods, combined with artificial neural network, fuzzy theory, genetic algorithm, GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality. At the same time, for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application, the four-grade standard for annual air quality evaluation has been proposed, and its practicality has been verified through experiments. By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm, it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model, both in efficiency and accuracy.
Air quality evaluation, fuzzy theory, genetic algorithm, BP neural network.
Cite This Article
M. . Ning, J. . Guan, P. . Liu, Z. . Zhang and G. M. P. . O’Hare, "Ga-bp air quality evaluation method based on fuzzy theory," Computers, Materials & Continua, vol. 58, no.1, pp. 215–227, 2019.
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