Environmental governance based on multiple linear regression fitting
Keywords:
Multiple Linear Regression; VIF; Grey Correlation Analysis; Entropy Weighting Method; TOPSIS; Simulated Annealing Algorithm, Minimum Number of Monitoring PointsAbstract
Regarding the construction of a mathematical model between air quality index (AQI) and different pollutant concentrations, we constructed pollutants respectively: PM2.5, PM10, CO, NO2, O3, SO2. firstly, we pre-processed the data in Annex 1, and analysed the data by visualising its missing values and boxplots to get no obvious outliers and missing values between the data. Secondly, we established multiple linear regression to fit the linearity between AQI and pollutant concentration, and fitted the expression: y=15.431 + 0.71*X_1 + 0.077*X_2 - 0.24*X_3 + 11.867* X_4+ 0.386*X_5 + 0.273*X_6, with the goodness-of-fit of as good, and T-test and F-test were conducted to prove the significant difference. prove that there are significant differences. Secondly, considering the influence of multicollinearity between the constructed indicators, we introduced the variance inflation factor VIF to test the indicators, and concluded that the construction of indicators is reasonable. At the same time, we take the pollutants as the sub-sequence and the AQI index as the parent sequence to construct the grey correlation analysis, to derive the grey correlation between each of its pollutants and the AQI, and to rank them, see Table 3.In this paper, based on the constructed regression equations, the collected air quality data of the national cities are fitted, and it is concluded that the ten cities with the best air quality are Dazhou City, Hegang City, Heihe City, Jiamusi City, Shuangyashan, Yichun, Nanchong, Qiqihar, and Suining.
References
[1] WANG Huiwen,MENG Jie. A predictive modelling approach for multiple linear regression[J]. Journal of Beijing University of Aeronautics and Astronautics,2007 (04):500-504.
[2] Chunya Hu. Research and application of optimisation index system of aging mine ventilation system based on variance expansion factor [D]. China University of Mining and Technology,2016.
[3] Sun Yugang. Research on grey correlation analysis and its application[D]. Nanjing University of Aeronautics and Astronautics,2007.
[4] YANG Yongping,WU Dianfa,WANG Ningling. Comprehensive evaluation of thermal power unit performance based on combined weight-advantageous solution distance method[J]. Thermal Power Generation,2016,45(02):10-15.
[5] Pang Feng. Principle of simulated annealing algorithm and application of the algorithm to optimisation problems[D]. Jilin University,2006.
[6] Hong Lihua,Zhou Weihong,Huang Qionghui. Research on data visualisation based on Python[J]. Science and Technology Innovation and Application,2022,12(33):36-40.
[7] Zhang Hongxian,Ma Yaofeng. Multiple regression prediction of China's inbound tourism market[J]. Resource Development and Market, 2005, 21(2):2.
[8] Huang Geng-Wen,Yang Lian-Yue,Lu Wei-Qun,et al. Multiple regression analysis of complications after resection of hepatocellular carcinoma[J]. Chinese Journal of Practical Surgery, 2006, 26(6):2.
[9] XIN H, WANG Congxu, LU Wei, et al. Multiple regression analysis of the correlation law between Chinese medicine physical quality and quality of life in 663 cases of hypertension[J]. China Journal of Basic Chinese Medicine, 2011, 17(7):2.
[10] MA Feng, YANG Fajun, CHEN Runqiao, et al. Multiple regression analysis of the impact of groundwater mining on ground subsidence in Tianjin[J]. Chinese Journal of Geological Hazards and Prevention, 2008, 19(2):4.
[11] Zhang MY, Yan HF, Li CH, et al. A correlation study of neuropsychological and related examinations in Alzheimer's disease: multiple regression analysis of (4) cognitive function tests with BEAM, BEP, and CT [J]. Shanghai Psychiatry, 1996.
[12] LIU Zheng,DANG Guangzhe,LIU Xiaojun. Application of multiple regression analysis in the prediction of coal seam gas content[J]. Mining Safety and Environmental Protection, 2013(05):57-60.
[13] Zhou Liandi. Multiple regression analysis and its application in ship research and design[M]. National Defence Industry Press, 1979.
[14] FU Fengling, ZHOU Shufeng, PAN Guangtang, et al. Multiple regression analysis of drought tolerance coefficient of maize[J]. Journal of Crops, 2003, 29(3):5.
[15] Xie Yayu,Zhang Yukun. Correlation and multiple regression analyses of life event factors and mental health status of ethnic minority college students in China[J]. Chinese Journal of Mental Health, 1993, 007(004):182-184.
[16] Wei H. Application of multiple regression analysis in spatial prediction of landslides[J]. Chang'an University, 2011.
[17] Jaccard. Interaction in multiple regression[M]. Gezi Publishing House,2012.
*******************Cite this Article*******************
APA:
Zou, Y., & Xu, J. (2023). Environmental governance based on multiple linear regression fitting. International Scientific Technical and Economic Research, 1(1), 1–13. http://www.istaer.online/index.php/Home/article/view/No.2301
GB/T 7714-2015:
Zou Yu, Xu Jinrun. Environmental governance based on multiple linear regression fitting[J]. International Scientific Technical and Economic Research, 2023, 1(1): 1–13. http://www.istaer.online/index.php/Home/article/view/No.2301
MLA:
Zou, Yu, and Jinrun Xu. "Environmental governance based on multiple linear regression fitting." International Scientific Technical and Economic Research, 1.1 (2023): 1-13. http://www.istaer.online/index.php/Home/article/view/No.2301
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