本文選題：機器學習 + 隨機森林��； 參考：《上海應用技術大學》2017年碩士論文

【摘要】：近年來,城市空氣污染問題愈發(fā)突出,大量的汽車尾氣、工業(yè)廢氣、揚塵等直接排放到城市環(huán)境中,遠遠超過了城市環(huán)境的自凈能力,導致空氣質(zhì)量下降,直接危及到城市居民的健康和安全,城市空氣質(zhì)量問題已經(jīng)引起了中國社會各界的高度重視。為了有效地控制空氣污染,提高城市的空氣質(zhì)量,首先一定要對城市環(huán)境空氣質(zhì)量進行科學合理的評價,使城市居民以及環(huán)境保護部門更加客觀地了解城市環(huán)境的空氣質(zhì)量,做出合理的生活安排及科學的防治措施。所以城市環(huán)境空氣質(zhì)量評價對于防治空氣污染發(fā)揮著重要的作用。隨著大數(shù)據(jù)時代的來臨以及人工智能的興起,傳統(tǒng)的評價方法已經(jīng)不能滿足對海量傳感器數(shù)據(jù)智能化處理的需求,大量學者開始基于大數(shù)據(jù),利用智能方法來評價城市環(huán)境的空氣質(zhì)量。機器學習是人工智能的分支,本文將機器學習引入到城市環(huán)境空氣質(zhì)量評價中,利用機器學習中的隨機森林算法來評價城市環(huán)境的空氣質(zhì)量,通過對隨機森林模型訓練,找到多種空氣污染物與空氣質(zhì)量等級之間的內(nèi)在映射關系,建立隨機森林評價模型,提高評價科學性和魯棒性。本文在仿真實驗時,將隨機森林評價模型與支持向量機,樸素貝葉斯和K最鄰近模型進行對比,仿真采用的數(shù)據(jù)為上海市2013年到2015年的部分空氣質(zhì)量真實數(shù)據(jù),仿真實驗得到了較好的效果,實驗結果表明評價方法效果最好,準確性最高可達99.69%,同時本文對隨機森林模型的性能進行了深入分析,進一步驗證了該評價方法的適用性與穩(wěn)定性,從分析可以看出本文模型的泛化誤差對特征變量個數(shù)不是很敏感,并且在準確性與時間復雜度之間有較好折衷,可以用于準確有效的評價城市環(huán)境的空氣質(zhì)量。
[Abstract]:In recent years, the problem of urban air pollution has become more and more prominent. A large number of automobile exhaust, industrial waste gas and dust are discharged directly into the urban environment, which far exceeds the self purification capacity of the urban environment, which leads to the decline of air quality, which directly endangers the health and safety of urban residents. The problem of urban air quality has already caused the high social circles in China. In order to effectively control air pollution and improve the air quality of the city, first of all, we must make a scientific and rational evaluation of the air quality of the urban environment, so that the urban residents and the environmental protection departments can understand the air quality of the urban environment more objectively and make reasonable living arrangements and scientific prevention measures. Air quality assessment plays an important role in preventing air pollution. With the advent of the era of large data and the rise of artificial intelligence, the traditional evaluation method can not meet the demand for intelligent processing of mass sensor data. A large number of scholars begin to use intelligent methods to evaluate the air of the urban environment based on large data. Quality. Machine learning is the branch of artificial intelligence. This paper introduces machine learning into urban environmental air quality evaluation, uses random forest algorithm in machine learning to evaluate the air quality of urban environment, and through the training of random forest model, the internal mapping relationship between air pollution and air quality is found. The stochastic forest evaluation model is established to improve the scientific and robust evaluation. In the simulation experiment, the stochastic forest evaluation model is compared with the support vector machine, the naive Bias and the K nearest model. The simulation data are the real data of the air quality in Shanghai from 2013 to 2015, and the simulation experiment is better. The results show that the evaluation method has the best effect and the maximum accuracy can reach 99.69%. At the same time, the performance of the random forest model is analyzed in this paper, and the applicability and stability of the evaluation method are further verified. There is a good trade-off between accuracy and time complexity, which can be used to evaluate the air quality of urban environment accurately and effectively.
【學位授予單位】：上海應用技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：X823

【參考文獻】

相關期刊論文 前4條

1 薛志鋼;郝吉明;陳復;柴發(fā)合;;歐美發(fā)達國家大氣污染控制經(jīng)驗[J];杭州(周刊);2016年05期

2 姜新華;劉霞;薛河儒;張存厚;;基于逐步回歸的空氣質(zhì)量影響因素分析——以呼和浩特市區(qū)為例[J];內(nèi)蒙古農(nóng)業(yè)大學學報(自然科學版);2015年02期

3 李鵬;;能源消費與我國的二氧化硫排放——兼論人口規(guī)模及技術進步對二氧化硫排放的影響[J];西北人口;2014年04期

4 廖銀念;蘇玉紅;艾尼瓦爾·買買提;;城市空氣質(zhì)量的模糊綜合評價——以烏魯木齊市為例[J];北方環(huán)境;2011年11期

相關博士學位論文 前1條

1 劉杰;北京大氣污染物時空變化規(guī)律及評價預測模型研究[D];北京科技大學;2015年

相關碩士學位論文 前6條

1 趙敏;基于層次分析法的地下水超采區(qū)劃分及壓采效果評價[D];太原理工大學;2016年

2 周家?guī)?基于多元統(tǒng)計和智能算法的上海市空氣質(zhì)量指數(shù)評價分析[D];蘭州大學;2016年

3 丁里;基于機器學習的P2P網(wǎng)絡流分類研究[D];江南大學;2015年

4 王露云;中國31個主要城市空氣質(zhì)量評價及主要污染物濃度預測[D];重慶師范大學;2014年

5 張良;石家莊市“十一五”期間環(huán)境空氣質(zhì)量變化趨勢分析及預測研究[D];河北科技大學;2013年

6 陳瑋;基于灰色聚類與模糊綜合評判的空氣質(zhì)量評價[D];華東師范大學;2012年

，

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