基于BP神经网络的大气污染物浓度预测

发布时间：2018-05-04 17:08

本文选题：BP神经网络 + MIV　；参考：《昆明理工大学》2017年硕士论文

【摘要】：近年来,空气污染日渐成为一个严峻的问题。空气质量恶化对人身健康和环境存在巨大的或者潜在的危害。因此,大气污染物浓度预报非常重要,它不仅对人们的日常生活有所帮助,而且对政府制定相关政策具有指导意义。2013年,国务院颁布了《大气污染防治行动计划》,要求各地建立监测预警体系,京津冀、长三角、珠三角及其他省、副省级市、省会城市均包括在内的城市或区域开展空气质量预报预警的工作。通过研究昆明市的污染物浓度预测模型,有助于昆明市空气质量预报预警工作的开展。以统计模型和机器学习模型为代表的非机理模型在污染物浓度预报中应用广泛,其中BP神经网络以其较强的非线性拟合能力在污染物浓度预测中广泛应用。本文利用BP神经网络结合变量筛选的方法建立了 SO2,NO2,O3,CO,PM10,PM2.5等6种污染物的浓度预测模型,并选取2014-1-1至2015-11-28时段,昆明市区6个环境监测点6种污染物浓度的监测数据建立了昆明市污染物均浓度预测模型。采用平均影响值(Mean Impact Value,MIV)的方法筛选出分别对6种污染物日浓度值有主要影响的变量,作为BP神经网络的输入变量,利用建立的预测模型分别对6种污染物的日浓度进行预测,并讨论MIV的方法在浓度预测中应用的可行性。(1)通过变量筛选的结果可以看出,前一日的其他污染物浓度对预报对象的浓度有较大影响;(2)BP神经网络模型的预测结果较好,预测的浓度水平和变化趋势与实测值的变化吻合度较高。标准化平均偏差NMB均小于18,标准化平均误差NMB均小于40,剩余标准差RMSE均小于30,相关系数R多大于0.6;(3)利用MIV方法对输入变量筛选,有助于BP神经网络模型预测精度的提高,个别模型如关上监测点N02,CO,碧鸡广场SO2,龙泉镇S02,呈贡新区SO2,东风东路S02、03的预测模型并不能提高预测精度;(4)各污染物的IAQI分指数的准确率较高,可以达到70%以上,首要污染物的准确可以达到50%左右,各点位的AQI均可达到65%以上。
[Abstract]:Air pollution has become a serious problem in recent years. The deterioration of air quality has great or potential harm to physical health and environment. Therefore, it is very important to predict the concentration of air pollutants. It is not only helpful to people's daily life, but also has a guiding significance for the government to make relevant policies for.2013 years, the State Council The action plan for the prevention and control of air pollution has been promulgated, which requires the establishment of monitoring and early warning system in all parts of the city, the Beijing Tianjin Hebei, the Yangtze River Delta, the Pearl River Delta and other provinces, the sub provincial cities and the provincial capital cities to carry out the air quality prediction and early warning in the cities and regions, which are included in the city and the provinces. The study of the pollutant concentration prediction model in Kunming will help the air quality in Kunming. The non mechanism model, represented by statistical model and machine learning model, is widely used in the prediction of pollutant concentration. The BP neural network is widely used in the prediction of pollutant concentration with its strong nonlinear fitting ability. In this paper, the method of BP neural network network combined with variable selection is used to establish SO2, NO 2, O3, CO, PM10, PM2.5, and other 6 kinds of pollutant concentration prediction model, and select the 2014-1-1 to 2015-11-28 period, the monitoring data of 6 pollutants concentration in 6 environmental monitoring points in Kunming City, establish the prediction model of the average concentration of pollutants in Kunming city. The average influence value (Mean Impact Value, MIV) is used to screen the daily concentration of 6 kinds of pollutants respectively. The variable which has the main influence on the degree value, as the input variable of the BP neural network, uses the established prediction model to predict the daily concentration of the 6 pollutants respectively, and discusses the feasibility of the application of the MIV method in the concentration prediction. (1) through the results of variable selection, it can be seen that the concentration of other pollutants on the previous day has the concentration of the forecast object. It has great influence; (2) the prediction results of BP neural network model are better. The predicted concentration level and change trend coincide with the measured values. The standard average deviation NMB is less than 18, the standard average error NMB is less than 40, the residual standard difference RMSE is less than 30, the relative number R is more than 0.6; (3) the MIV method is used to screen the input variable sieve. Selection is helpful to improve the prediction accuracy of BP neural network model. Some models, such as monitoring point N02, CO, BBI square SO2, Longquan town S02, Chenggong New Area SO2, Dongfeng East Road S02,03 prediction model, can not improve the prediction accuracy. (4) the accuracy of IAQI sub index of each pollutant is higher than 70%, and the primary pollutant is accurate. At about 50%, the AQI of each point can reach more than 65%.

【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X51

【参考文献】