Prediction of Air Quality Using Time Series Data Mining

被引：2

作者：

Yadav, Mansi ^{[1
]}

Jain, Suruchi ^{[1
]}

Seeja, K. R. ^{[1
]}

机构：

[1] Indira Gandhi Delhi Tech Univ Women, Dept Comp Sci & Engn, Delhi, India

来源：

INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING AND COMMUNICATIONS, VOL 2 | 2019年 / 56卷

关键词：

Continuous target sequential pattern discovery; Time series data mining; Air pollution prediction;

D O I：

10.1007/978-981-13-2354-6_2

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The recent bout of increased air pollution in Delhi has just made the task of identifying and controlling the causes of air pollution an extremely crucial task. In this paper, an Apriori-based association rule mining algorithm, which is a modified version of the Continuous Target Sequential Pattern Discovery (CTSPD), is used to generate a set of association rules that help in predicting the concentration of air pollutants. This algorithm considers the temporal aspect of the data and hence gives the rules with continuous events only as the result. The performance of the algorithm is evaluated by mining the air quality and meteorological data from Anand Vihar, New Delhi, over the period September 1, 2015 to August 31, 2016. The prediction of the proposed algorithm is compared with that of an existing prediction system, SAFAR and found that the proposed algorithm is more accurate than SAFAR.

引用

页码：13 / 20

页数：8

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