深圳市生活垃圾产生现状及清运量预测研究
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摘要
在我国,随着城市经济的飞速发展,城市生活垃圾所引发的社会问题及环境问题也日益凸显出来,这在地少人多的深圳显得异常突出。为了解决这个棘手的问题,给市民营造美好的生活环境,深圳市政府对城市生活垃圾问题越来越重视,对生活垃圾管理系统的开展了多层面的研究,广泛建设生活垃圾配套处理工程。因此,准确预测城市生活垃圾清运量就显得尤为重要。
本文分析了深圳市2000~2009年的生活垃圾清运量的变化情况,对影响其变化的内在因素进行灰关联分析,通过阅读文献,对国内外垃圾预测方法及模型进行全面的归纳总结,选定由多元线性回归模型、逐步回归模型、GM(1,1)、GPPM构成的变权重组合模型,对深圳市“十二五”期间的城市生活垃圾清运量进行预测分析。
研究结果表明,本文提出的非负可变加权系数组合模型成功地融合了各单项预测模型的优点,摒弃了各自的不足,其预测效果均显著优于各单项预测模型,能够客观真实地反映近十年深圳城市生活垃圾清运量的变化及发展,其结果可作为当地政府部门的决策依据。预计在“十二五”期间,深圳城市生活垃圾清运量每年将增加30万吨以上,平均增长率为6.4%左右,到2015年末,深圳市城市生活垃圾清运量将会达到660万吨左右。
In our country, due to the rapid development of urban economy, the social problems and environmental issues causesd by municipal solid waste are becoming increasingly prominent, which appears especially serious in Shenzhen where there is less land and large population. In order to solve this troublesome problem and beautify living environment for the citizens, the local government is taking more and more seriously the issues, conducting extensive research on municipal solid waste management system, and having waste disposal facilities constructed. So, the accurate prediction of the municipal solid waste delivering quantity plays an especially important role in municipal planning projects and city management.
This thesis studied the change of MSW delivering quantity from the year 2000 to 2009 in Shenzhen, and the internal factors which affect the municipal solid waste delivering quantity was analyzed by grey incidence analysis. Comprehensive analysis and induction to all home and abroad prediction methods after searching literature, then chose combination forecasting model with non-negative variable weights that consisted of multiple linear regression model, stepwise regression model, GM(1, 1) and GPPM to predict and analyze municipal solid waste delivering quantity in Shenzhen city from the year 2011 to 2015.
The research manifested that, the combination forecasting model with non-negative variable weights was obviously superior to every single forecasting model, which could objectively and factually reflect the change and development of MSW delivering quantity from the year 2000 to 2009 in Shenzhen, whose forecasting results was able to become decision-making evidence when local authorities adjusted the policy. According to the combination predicting model, the municipal solid waste delivering quantity will increase more than 300 thousand tons every year and the average annual growth rate will reach 6. 4%, during the period of the“12th 5 year-plan”. Until the end of the year 2015, the MSW delivering quantity in Shenzhen will be approximately 6. 6million tons.
本文分析了深圳市2000~2009年的生活垃圾清运量的变化情况,对影响其变化的内在因素进行灰关联分析,通过阅读文献,对国内外垃圾预测方法及模型进行全面的归纳总结,选定由多元线性回归模型、逐步回归模型、GM(1,1)、GPPM构成的变权重组合模型,对深圳市“十二五”期间的城市生活垃圾清运量进行预测分析。
研究结果表明,本文提出的非负可变加权系数组合模型成功地融合了各单项预测模型的优点,摒弃了各自的不足,其预测效果均显著优于各单项预测模型,能够客观真实地反映近十年深圳城市生活垃圾清运量的变化及发展,其结果可作为当地政府部门的决策依据。预计在“十二五”期间,深圳城市生活垃圾清运量每年将增加30万吨以上,平均增长率为6.4%左右,到2015年末,深圳市城市生活垃圾清运量将会达到660万吨左右。
In our country, due to the rapid development of urban economy, the social problems and environmental issues causesd by municipal solid waste are becoming increasingly prominent, which appears especially serious in Shenzhen where there is less land and large population. In order to solve this troublesome problem and beautify living environment for the citizens, the local government is taking more and more seriously the issues, conducting extensive research on municipal solid waste management system, and having waste disposal facilities constructed. So, the accurate prediction of the municipal solid waste delivering quantity plays an especially important role in municipal planning projects and city management.
This thesis studied the change of MSW delivering quantity from the year 2000 to 2009 in Shenzhen, and the internal factors which affect the municipal solid waste delivering quantity was analyzed by grey incidence analysis. Comprehensive analysis and induction to all home and abroad prediction methods after searching literature, then chose combination forecasting model with non-negative variable weights that consisted of multiple linear regression model, stepwise regression model, GM(1, 1) and GPPM to predict and analyze municipal solid waste delivering quantity in Shenzhen city from the year 2011 to 2015.
The research manifested that, the combination forecasting model with non-negative variable weights was obviously superior to every single forecasting model, which could objectively and factually reflect the change and development of MSW delivering quantity from the year 2000 to 2009 in Shenzhen, whose forecasting results was able to become decision-making evidence when local authorities adjusted the policy. According to the combination predicting model, the municipal solid waste delivering quantity will increase more than 300 thousand tons every year and the average annual growth rate will reach 6. 4%, during the period of the“12th 5 year-plan”. Until the end of the year 2015, the MSW delivering quantity in Shenzhen will be approximately 6. 6million tons.
引文
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[13] Laura Sokka, Riina Antikainen, Pekka E. Kauppi. Municipal solid waste production and composition in Finland—Changes in the period 1960–2002 and prospects until 2020[J]. Resources, Conservation and Recycling, 2007, 50: 475-488
[14] Sara Ojeda Benítez, Gabriela Lozano-Olvera, Raúl Adalberto Morelos, Carolina Armijo de Vega. Mathematical modeling to predict residential solid waste generation[J]. Waste Management, 2008, 28: S7-S13
[15] Noori R., Abdoli M. A., Ghazizade M., et al. Comparison of Neural Network and Principal Component-Regression Analysis to Predict the Solid Waste Generation in Tehran[J]. Iranian Journal Of Public Health, 2009, 38(1): 74-84
[16] Roohollah Noori, Abdulreza Karbassi, Mohammad Salman Sabahi. Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction[J]. Journal of Environmental Management, 2010, 91: 767-771
[17]李景.上海市区生活垃圾预测[J].上海环境科学, 1986(8): 9-12
[18]吴赤蓬,宋士德,余载礼等.广州市生活垃圾预测中的两类模型应用[J].数理医药学杂志, 1993, 6(3): 5-8
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[20]范常忠,张淑娟.佛山市生活垃圾的灰色预测与构成特征研究[J].环境科学研究, 1997, 10(4): 61-64
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[22]向盛斌.城市居民生活垃圾影响因素分析及产量预测[J].环境卫生工程, 1998, 6(1): 7-12
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