A study on environmental governance in China based on GA-BP neural network and TOPSIS method
Keywords:
Genetic algorithm; BP neural network; Spearman correlation; Theory of moment estimation; TOPSIS; Gaussian kernel support vector machineAbstract
Regarding the construction of a mathematical model between air quality index (AQI) and different pollutant concentrations, firstly, a genetic algorithm was used to optimize the BP neural network model with PM2.5, PM10, 〖SO〗_2, CO, 〖NO〗_2 and O_3 as the main air pollutants, and then the air pollution in Beijing from 2015 to 2021 was used as the validation object, and it was found that the fitted R-squared on the basis of 20% test set is greater than 0.95 or above, and finally the Spearman correlation model is used to analyze the main pollutants associated with AQI index to provide solutions for the subsequent treatment of air pollution. Regarding the construction of a comprehensive water quality evaluation model, firstly, the weight integration method based on the moment estimation theory was used to assign subjective and objective weights to the indicators, and dissolved oxygen, temperature, turbidity, ammonia nitrogen, permanganate index and hydrogen ion concentration index were used as indicators for evaluating water quality, and then the optimal weights of each indicator were derived as 7.766%, 9.509%, 37.962%, 19.666%, 17.726% and 7.7371%, and finally the comprehensive evaluation of water quality in each city was carried out by TOPSIS method. Regarding the construction of urban noise pollution monitoring network, firstly, a genetic algorithm based on Gaussian kernel support vector machine was used to optimize the model, and Guangzhou was used as the validation object to solve the Gaussian response surface with the building density as the noise index, and the optimal number of monitoring points of 10 was obtained by using genetic algorithm for optimization.
References
[1] He, T. I., Tian, X. Z., Li, S. N., et al. Improved BP neural network based on swarm algorithm for wind power prediction[J]. Journal of Electric Power Science and Technology, 2018,33(04):22-28.
[2] Wu, Kaifeng, Zhang, Lixin, Kan, Xi, Wang, Jun'ang, Wang, Sai. A pressure sensor calibration method based on improved GA-BP neural network[J]. Foreign Electronic Measurement Technology,2023,42(02):38-44.
[3] Yu Qun, Huo Shodong, He Jian, Li Lin, Zhang Jianxin, Feng Yuyao. Trend prediction of power grid outages in China based on Spearman correlation coefficient and system inertia [J/OL]. Chinese Journal of Electrical Engineering:1-12[2023-06-04].
[4] Gao Mingyu, Cai Linhui, Sun Changcheng, Liu Caiming, Zhang Zhaofei, Dong Zhekang, He Zhiwei, Gao Weiwei. A short-circuit fault detection algorithm based on Spearman rank correlation combined with neural networks inside a battery pack[J]. Journal of Electronics and Information,2022,44(11):3734-3747.
[5] Scientific Platform Serving for Statistics Professional 2021. SPSSPRO. (Version 1.0.11)[Online Application Software]. Retrieved from https://www.spsspro.com.
[6] Wen, Shao-Chun, Luo, Fei, Fu. A review on the application of genetic algorithms in artificial neural networks[J]. Computational Technology and Automation, 2001(02):1-5.
[7] Gan Longxiong,Zhang Huaizhi,Lu Talent,et al. Water traffic safety factors based on entropy power method[J]. China Navigation,2021,44(2):53-58.
[8] Liu Panpan, Ren Guangyue, Duan Xu, Li Linlin, Zhao Lujie, Ren Xing, Miao Junwei. Evaluation of the quality and flavor of white radish by different drying methods based on the coefficient of variation method[J/OL]. Food and Fermentation Industry:1-11[2022-03-31].DOI:10.13995/j.cnki.11-1802/ts.029017.
[9] Jiang Shiquan,Liu Sifeng,Liu Zhongman. A general gray number correlation decision model based on moment estimation theory with applications[J]. Statistics and Decision Making,2019,35(24):29-33.DOI:10.13546/j.cnki.tjyjc.2019.24.006.
[10] Xue, L., Su, Chao, Cui, Chaoqun, and Sun, Yan-Ding. Application of optimal combination assignment method under moment estimation theory in mineral resource evaluation[J]. China Mining,2017,26(04):41-46.
[11] Fu Jia-Feng, Liu Qian, Ma Zhan-Yun, Ying Na. A comprehensive evaluation of carbon peaking capacity of 30 provinces in China[J]. Ecological Economics,2023,39(06):18-24.
[12] Yin Yanchu, Zou permanently, Du Tali, Zhang Yuewen. SSA-SVM based fault diagnosis of cryogenic freshwater cooling system of ships [J/OL]. Computer Simulation:1-7[2023-06-04].
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APA:
Liu, C. (2023). A study on environmental governance in China based on GA-BP neural network and TOPSIS method. International Scientific Technical and Economic Research, 1(2), 1–20. http://www.istaer.online/index.php/Home/article/view/No.2306
GB/T 7714-2015:
Liu Chuntong. A study on environmental governance in China based on GA-BP neural network and TOPSIS method[J]. International Scientific Technical and Economic Research, 2023, 1(2): 1–20. http://www.istaer.online/index.php/Home/article/view/No.2306
MLA:
Liu, Chuntong. "A study on environmental governance in China based on GA-BP neural network and TOPSIS method." International Scientific Technical and Economic Research, 1.2 (2023): 1-20. http://www.istaer.online/index.php/Home/article/view/No.2306
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