Applying Artificial Neural Networks to Short-Term PM2.5 Forecasting Modeling

被引：2

作者：

Oprea, Mihaela ^{[1
]}

Mihalache, Sanda Florentina ^{[1
]}

Popescu, Marian ^{[1
]}

机构：

[1] Petr Gas Univ Ploiesti, Automat Control Comp & Elect Dept, Ploiesti, Romania

来源：

ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2016 | 2016年 / 475卷

关键词：

Artificial neural networks; Forecasting modeling; Air pollution; PM2.5 air pollutant short-term forecasting; Model based forecasting protocol; PREDICTION;

D O I：

10.1007/978-3-319-44944-9_18

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 with suspended particles from PM2.5 fraction represents an important factor to increasing atmospheric pollution degree in urban areas, with a significant potential effect on the health of vulnerable people such as children and elderly. PM2.5 air pollutant concentration continuous monitoring represents an efficient solution for the environment management if it is implemented as a real time forecasting system which can detect the PM2.5 air pollution trends and provide early warning or alerting to persons whose health might be affected by PM2.5 air pollution episodes. The forecasting methods for PM concentration use mainly statistical and artificial intelligence-based models. This paper presents a model based protocol, MBP - PM2.5 forecasting protocol, for the selection of the best ANN model and a case study with two artificial neural network (ANN) models for real time short-term PM2.5 forecasting.

引用

页码：204 / 211

页数：8

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