發(fā)布時(shí)間：2018-07-05 00:17

  本文選題：整體經(jīng)驗(yàn)?zāi)B(tài)分解 + 隨機(jī)森林回歸��； 參考：《長春工業(yè)大學(xué)》2017年碩士論文

【摘要】：近年來,PM2.5已經(jīng)嚴(yán)重影響到了人們的生活和出行,大氣環(huán)境污染問題較為嚴(yán)重。我國一些大中型城市的年均PM2.5濃度已經(jīng)遠(yuǎn)遠(yuǎn)高于世界衛(wèi)生組織環(huán)境空氣質(zhì)量的指導(dǎo)值10ug/m3,使得患心血管和呼吸系統(tǒng)疾病的人逐年增加。PM2.5作為形成霧霾的主要污染物,已成為城市大氣污染治理的首要目標(biāo)。因此,研究PM2.5的變化趨勢,掌握其變化規(guī)律,建立合理精確的預(yù)測模型,對其進(jìn)行短期預(yù)測,不僅能提醒人們及時(shí)采取有效措施,保護(hù)身體健康。而且對調(diào)控一些社會(huì)活動(dòng)和對重度環(huán)境污染帶來的危害進(jìn)行提前預(yù)警具有一定的現(xiàn)實(shí)意義。本文以構(gòu)建合理的PM2.5預(yù)測模型為目的,采用整體經(jīng)驗(yàn)?zāi)B(tài)分解法對北京市PM2.5時(shí)間序列進(jìn)行處理,研究了北京市PM2.5的波動(dòng)規(guī)律及其周期性變化。首先,采用整體經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD)方法對PM2.5數(shù)據(jù)進(jìn)行分解,對PM2.5的變化趨勢、波動(dòng)和周期性變化進(jìn)行分析;其次,運(yùn)用主成分分析回歸(PCR)方法、支持向量機(jī)回歸(SVR)的預(yù)測方法、自回歸滑動(dòng)平均(ARIMA)的方法、隨機(jī)森林(RF)回歸等方法對北京市PM2.5進(jìn)行短期預(yù)測;最后,提出基于整體經(jīng)驗(yàn)?zāi)B(tài)分解和隨機(jī)森林的PM2.5預(yù)測方法,即通過對北京市PM2.5數(shù)據(jù)進(jìn)行EEMD分解,對分解后得到的固有模態(tài)函數(shù)和PM2.5的影響因子進(jìn)行隨機(jī)森林建模。通過比較ARMA、SVR、PCR、RF和EEMD-RF這五種方法對PM2.5預(yù)測的精確度,以探究最佳的PM2.5預(yù)測模型。
[Abstract]:In recent years, PM2.5 has seriously affected people's life and travel. The average annual concentration of PM2.5 in some large and medium-sized cities in China is much higher than that of the WHO's guiding value of ambient air quality, which makes the number of people suffering from cardiovascular and respiratory diseases increase year by year as the main pollutant to form haze. It has become the primary target of urban air pollution control. Therefore, studying the change trend of PM2.5, mastering its changing law, establishing a reasonable and accurate prediction model and forecasting it in the short term can not only remind people to take effective measures in time to protect their health. Moreover, it is of practical significance to regulate and control some social activities and to forewarn the harm caused by heavy environmental pollution. In order to construct a reasonable prediction model of PM2.5, the global empirical mode decomposition (EMD) method is used to deal with the PM2.5 time series in Beijing, and the fluctuation law and periodic variation of PM2.5 in Beijing are studied. Firstly, the global empirical mode decomposition (EEMD) method is used to decompose PM2.5 data, and the variation trend, fluctuation and periodicity of PM2.5 are analyzed. Secondly, the prediction method of support vector machine regression (SVR) is presented by principal component analysis (PCR). The methods of autoregressive moving average (Arima) and random forest (RF) regression are used to predict PM2.5 in Beijing in the short term. Finally, a forecasting method of PM2.5 based on global empirical mode decomposition and stochastic forest is proposed, which is based on EEMD decomposition of PM2.5 data in Beijing. The natural mode function and the influence factor of PM2.5 are analyzed by stochastic forest modeling. In order to explore the best prediction model of PM2.5, the accuracy of PM2.5 prediction was compared between ARMA-SVRN PCRN RF and EEMD-RF.
【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：X831

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 侯俊雄;李琦;朱亞杰;馮逍;毛曦;;基于隨機(jī)森林的PM2.5實(shí)時(shí)預(yù)報(bào)系統(tǒng)[J];測繪科學(xué);2017年01期

2 艾洪福;潘賀;李迎斌;;關(guān)于空氣霧霾PM_(2.5)含量預(yù)測優(yōu)化研究[J];計(jì)算機(jī)仿真;2017年01期

3 高僮;陳波濤;張海峰;;基于支持向量機(jī)回歸的短期風(fēng)速預(yù)測方法的研究[J];科技創(chuàng)新與應(yīng)用;2016年36期

4 徐繼紅;;基于EEMD模型的干旱區(qū)降水變化趨勢分析[J];水資源開發(fā)與管理;2016年04期

5 余坤勇;姚雄;邱祈榮;劉健;;基于隨機(jī)森林模型的山體滑坡空間預(yù)測研究[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2016年10期

6 張少杰;游洋;;基于主成分回歸分析的需水預(yù)測研究[J];海河水利;2016年03期

7 付倩嬈;;基于多元線性回歸的霧霾預(yù)測方法研究[J];計(jì)算機(jī)科學(xué);2016年S1期

8 蔣夢琪;;京津冀地區(qū)PM2.5濃度與氣象因子的關(guān)系[J];山東化工;2016年09期

9 李洪珠;孫佳月;;一種基于EEMD的新型諧波誤差分析策略[J];計(jì)算機(jī)工程與科學(xué);2016年04期

10 董小剛;周明眉;秦喜文;劉媛媛;張瑜;;基于EMD的PM2.5趨勢分析[J];長春工業(yè)大學(xué)學(xué)報(bào);2016年02期

相關(guān)博士學(xué)位論文 前1條

1 李海生;支持向量機(jī)回歸算法與應(yīng)用研究[D];華南理工大學(xué);2005年

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