兰州大学机构库 >数学与统计学院
悬浮颗粒物PM10与PM2.5的统计分析与预测
Alternative TitleStatistical analysis and forecsting of suspended particulate matters,
秦珊珊
Thesis Advisor王建州
2014-05-31
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
KeywordPM10与PM2.5 关联性分析 差分自回归移动平均模型(ARIMA) 神经网络(BPNN) ,统计预测
Abstract随着工业化和城市化的发展,化石燃料地不断消耗,致使空气质量日益恶化,以致雾霾天气越来越高频率与大范围的在全国发生,对全国人民的正常生活、工作以及身体健康造成了不良影响。这已经成为政府与民众共同关注的热点问题,也是亟需研究与解决的问题。雾霾产生以及具有危害的最主要原因即是空气中存在的悬浮颗粒物,PM10与PM2.5。因此,对空气中的悬浮颗粒物浓度进行检测,以及进一步基于检测数据进行科学有效的预测已经可不容缓。 首先,本文从气象因素与空气污染物等角度出发,探究影响悬浮颗粒物PM10与PM2.5浓度的关键因素。其次,我们利用Granger因果关系检验对PM10、PM2.5与气象因素及空气污染物之间的动态相关关系也进行了探究。再次,为了能够更好的做好空气中悬浮颗粒物PM10与PM2.5的预报工作,本文提出了传统的ARIMA 模型与基于人工智能优化神经网络模型对PM10与PM2.5进行预测,用于探究其预测能力。最后,考虑到传统的预测结果一般都是确定的预测值,而忽视了预测结果的波动范围。本文首次提出利用ARIMA模型与智能优化的混合神经网络模型对空气中悬浮颗粒物PM10与PM2.5浓度的区间预测进行了探究,旨在对未来的PM10与PM2.5浓度的波动范围给出合理的预测。
Other AbstractWith the rapid urbanization and industrialization, fossil fuel consumption in China is gradually increasing, resulting in the worsening of air quality. Recently, pollutant hazes occur more frequently and widely around the country, which makes a negative influence on people’s normal life and activities. It has become a social concern and needs to be researched and solved urgently. Suspended particulate matters, PM10 and PM2.5, is the main reasons of the haze, resulting in harmfulness to human beings. Thus, it is necessary to detect and record the concentrations of suspended particulates, and based on the recorded data we need to forecast its future trends and variations scientifically and effectively. Firstly, from the prospect of meteorological factors and air pollutants, this paper explores the key factors influencing the concentration of PM10 and PM2.5. Secondly, the Granger causality test tool is proposed to examine the dynamic relationship between particulate matters and meteorological factors together with air pollutants. Thirdly, in order to obtain better prediction of PM10 and PM2.5, this paper proposes ARIMA model and artificial intelligence algorithm as well as Neural Networks based hybrid models to conduct PM10 and PM2.5 forecasting. Moreover, considering the fact that most traditional predictions are generally deterministic, that is, a certain predictive value. It is not reasonable to be regardless of the possible variations of the forecasts, which may convey detailed information. Therefore, this paper initially proposes ARIMA and CS-based BPNN model to construct interval forecast models, aimed at supplying reasonable fluctuation ranges of the PM10 and PM2.5 concentrations.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/224553
Collection数学与统计学院
Recommended Citation
GB/T 7714
秦珊珊. 悬浮颗粒物PM10与PM2.5的统计分析与预测[D]. 兰州. 兰州大学,2014.
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