Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers

被引：10

作者：

Bougoudis, Ilias ^{[1
]}

Demertzis, Konstantinos ^{[1
]}

Iliadis, Lazaros ^{[1
]}

Anezakis, Vardis-Dimitris ^{[1
]}

Papaleonidas, Antonios ^{[1
]}

机构：

[1] Democritus Univ Thrace, 193 Pandazidou St, N Orestiada 68200, Greece

来源：

ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2016 | 2016年 / 629卷

关键词：

Pollution of the atmosphere; Air quality; Semi-supervised learning; Semi-supervised clustering; Semi-supervised classification; Air pollution; OZONE CONCENTRATIONS; PREDICTION;

D O I：

10.1007/978-3-319-44188-7_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Air pollution is directly linked with the development of technology and science, the progress of which besides significant benefits to mankind it also has adverse effects on the environment and hence on human health. The problem has begun to take worrying proportions especially in large urban centers, where 60,000 deaths are reported each year in Europe's towns and 3,000,000 worldwide, due to long-term air pollution exposure (exposure of the European Agency for the Environment http://www.eea.europa.eu/). In this paper we propose a novel and flexible hybrid machine learning system that combines Semi-Supervised Classification and Semi-Supervised Clustering, in order to realize prediction of air pollutants outliers and to study the conditions that favor their high concentration.

引用

页码：51 / 63

页数：13

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