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基于数据挖掘技术的空气质量指数预测研究

摘要

近年来空气污染状况日益严重，频繁出现的重污染天气对人们的日常

生活乃至生命健康都造成了严重影响。空气质量指数（AQI）的预报作为公

众知晓未来空气状况最直接的途径，不仅能为环保部门开展空气环境治理

工作提供指导，同时也能够提醒公众合理规避重度污染天气。但由于影响

空气质量的因素较多，气象环境的复杂性、污染物之间的非线性关系都为

空气质量指数的预报造成了困难。且传统的潜势预报、数值预报也并未做

到对海量历史数据的充分利用，因此在预报中依然存在着准确度不高、实

效性不强等局限性。

针对这些问题，论文以西安市

2014

年

月

日

-2016

年

月

日间

空气质量监测站点每日所采集的数据为研究对象，使用数据挖掘技术中的

灰色系统理论以及神经网络技术，建立了两种空气质量指数预测模型。并

通过对模型的进一步优化，提高预测准确度以及结果的可接受度。论文的

主要工作如下：

（1）选择合适的指标数据参与空气质量指数预测模型的建立。指标的

选取应从环境要素以及气象条件两方面考虑。根据环境空气质量标准，选

取 SO

、PM

2.5

、PM

、CO、O

、NO

、AQI 等 7 项污染指标，以及风力级

别、平均湿度、最高温度、最低温度、平均温度等 5 项气象因素共计 12 个

指标作为此次预测建模的主要研究对象。

（2）对参与预测建模的数据进行预处理。首先，需要对监测站点采集

的数据进行初步筛选，在剔除无效和缺失数据后，保留 940 条数据。其次，

考虑到不同影响因子的数据取值范围以及单位存在明显差异，会对预测结

果产生影响，因此采用 mapminmax 函数对数据进行归一化处理，以消除各

类数据之间的量纲差别。

（

）建立基于

GM(1,1)

的空气质量指数预测模型。根据灰色预测方法

的适用特性，以空气质量指数的历史数据作为研究对象，建立基于 GM(1,1)

预测模型。通过对预测结果进行分析，评估灰色系统理论在空气质量预测

上的效果。

（4）建立基于 BP 神经网络的空气质量指数预测模型。根据参与建模

的数据调整 BP 神经网络的参数，确定合理的网络结构，并在 MATLAB 平

台下编写完整预测程序，对空气质量指数进行预测。计算结果的平均绝对

万方数据

RESEARCH ON AIR QUALITY INDEX

FORECASTING BASED ON DATA

MINING TECHNOLOGY

ABSTRACT

In recent years, air pollution is getting worse, and frequent heavy pollution

weather has seriously affected people's daily life and health. The air quality

index (AQI) forecast which is the most direct way for the public to know the

future air quality, not only provides guidance for the environmental protection

department to carry out air pollution management, but also reminds the public to

avoid the severe pollution weather. However, there are so many factors which

make the AQI forecast become difficult, such as complexity and variability of

meteorological environment, the nonlinear relationship between pollutants, etc.

And the traditional potential forecast and numerical forecast do not make full

use of the massive historical data. Therefore, there are still some limitations in

AQI forecast, such as low accuracy and low efficiency.

Aiming at these issues, this thesis acquired the data by Xi'an air quality

monitoring station from 2014/1/28 to 2016/8/29, and took the data as research

object. We established two models of AQI by using the grey system theory and

neural network technology which belong to data mining. Through the further

optimization of the model, the accuracy of prediction and the acceptability of

the results can be improved. The main work of this paper is as follows:

(1)We selected appropriate index data to establish AQI prediction model.

The selection of indicators should be considered from environmental factors and

meteorological conditions. According to the air quality standards, including 7

pollution indicators such as SO

, PM

2.5

, PM

, CO, O

, NO

, AQI, as well as 5

meteorological factors such as the wind level, mean humidity, highest, lowest,

mean temperature, as the main research objects of the forecast model.

(2)We preprocessed the data involved in predictive model. Firstly, the data

collected from the monitoring site was initially screened. After eliminating

invalid and missing data, 940 data was retained. Next, in view of the different

influence factors whose data range and unit has obvious differences will have

万方数据

impacts on the prediction results. Thus, we used the mapminmax function to

normalize the data to eliminate the dimensional difference between various

kinds of data.

(3)We established an AQI prediction model based on GM(1,1). According

to the characteristics of grey prediction method, putting the historical data of

AQI as the research object, we established the prediction model based on

GM(1,1). The prediction results were analyzed to evaluate the effect of grey

system theory on air quality prediction.

(4)We established an AQI prediction model based on BP neural network.

According to the data of the model to adjust the parameters of the BP neural

network, we determined a reasonable network structure, and wrote a complete

prediction program on MATLAB to make the AQI forecast come true. In order

to evaluate the prediction model, we calculated the average absolute percentage

error, acceptability and the correct rate of air quality grade.

(5)We optimized the BP neural network prediction model. In order to

improve the accuracy of prediction, principal component analysis and genetic

algorithm were used to optimize the prediction model of BP neural network. On

the one hand, the complexity of the training process is reduced by decreasing

the dimension of the input variables. On the other hand, we improved the

predication ability of the model by optimizing the initial parameters of BP

neural network. Finally, the prediction model of the PCA-BP and GA-BP neural

network were compared with the single BP neural network prediction model.

The results show that the grey forecast model is of great significance to the

prediction of air quality index in the case of less effective information. In the

situation of abundant data, the mean absolute percentage error of a single BP

neural network prediction model is 21.96%. After the optimizing of the BP

neural network by principal component analysis and genetic algorithm, the error

is reduced by 2.23% and 3.67% respectively, and the acceptability of the results

and the accuracy of air quality forecast are also improved. Even though the

optimized model cannot completely accurately predict the air quality index, the

results still have some reference value.

KEY WORDS: Air quality index, grey system theory, BP neural network,

principal component analysis, genetic algorithm

万方数据

摘要............................................................................................................................................ I

ABSTRACT...............................................................................................................................III

............................................................................................................................................ I

1 绪论..........................................................................................................................................1

1.1

研究的目的和意义

...................................................................................................... 1

1.2

国内外应用研究现状

.................................................................................................. 3

1.2.1 国外研究现状................................................................................................... 3

1.2.2

国内研究现状

................................................................................................... 4

1.3

本文的主要工作及内容安排

...................................................................................... 5

2 相关理论与技术基础............................................................................................................. 7

2.1

数据挖掘与人工智能

.................................................................................................. 7

2.1.1

数据挖掘的概述与应用

...................................................................................7

2.1.2 人工智能的概述与应用...................................................................................7

2.2

人工神经网络

.............................................................................................................. 8

2.2.1

人工神经网络概述

........................................................................................... 8

2.2.2 人工神经元模型............................................................................................... 9

2.2.3

人工神经网络的分类

..................................................................................... 11

2.2.4

人工神经网络的学习规则

.............................................................................12

2.3 灰色系统理论............................................................................................................ 13

2.3.1

灰色系统理论概述

......................................................................................... 13

2.3.2

灰色预测建模及其特点

.................................................................................13

2.4 主成分分析法............................................................................................................ 14

2.5

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