生活垃圾产量灰色预测模型优化研究

发布时间：2018-04-04 03:36

本文选题：城市生活垃圾　切入点：灰色模型　出处：《华中科技大学》2015年硕士论文

【摘要】：我国城市化和工业化的不断发展促进了经济发展和社会进步,与此同时,也带来了城镇人口数量的大幅提高以及城市生活垃圾产量的剧增,大量累积未处理的垃圾使城市“垃圾围城”现象突出,为处理垃圾而进行的处理设施选址项目使“邻避效应”问题更加突出。只有对生活垃圾产量进行准确的预测,才能明确废弃物设施需求,并由此确定废弃物设施规模、投资额以及运行费用,同时,政策制定者才能更好的了解到问题所在,从而做出有效、实用的决策。因此,生活垃圾产生量预测是城市生活垃圾全过程管理的定量基数,是废弃物管理中必不可少的一个部分。本文首先分析了影响城市生活垃圾产量的各类因素,选择内在因素作为主要考虑因素,在利用Pearson关联分析和灰色关联分析筛选出主要影响因素的基础上,建立了非线性优化GM(1,N)模型,以昆明市为例,对城市生活垃圾产量进行预测,并将拟合结果和检验结果与传统GM(1,1)、GM(1,N)模型进行对比。结果表明,非线性优化模型在拟合精度上,优于传统灰色模型,这表明了该模型的可行性与优化性。
[Abstract]:The continuous development of urbanization and industrialization in our country has promoted economic development and social progress. At the same time, it has also brought about a substantial increase in the number of urban population and a sharp increase in the output of municipal solid waste.A large amount of untreated garbage makes the phenomenon of "garbage besieging" prominent, and the problem of "neighborhood avoidance" is more prominent because of the site selection project of treatment facilities for the disposal of garbage.It is only by accurately predicting the production of domestic waste that the demand for waste facilities can be determined, and the scale, investment and operating costs of waste facilities will be determined, and at the same time, policy makers will be able to better understand the problem.In order to make effective, practical decisions.Therefore, the prediction of municipal solid waste (MSW) production is the quantitative base of municipal solid waste (MSW) management in the whole process, and it is an indispensable part of MSW management.In this paper, we first analyze the factors that affect the production of municipal solid waste (MSW), select the internal factors as the main factors, and select the main influencing factors based on the Pearson correlation analysis and grey correlation analysis.A nonlinear optimized GM1N) model is established. Taking Kunming as an example, the output of municipal solid waste (MSW) is forecasted. The fitting results and the test results are compared with the traditional GMGM1N model.The results show that the nonlinear optimization model is superior to the traditional grey model in fitting accuracy, which indicates the feasibility and optimization of the model.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：X799.3

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