Applying Artificial Neural Networks to Short-Term PM2.5 Forecasting Modeling - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2016

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

Mihaela Oprea
  • Function : Author
  • PersonId : 1008260
Marian Popescu
  • Function : Author
  • PersonId : 1011957

Abstract

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.
Fichier principal
Vignette du fichier
430537_1_En_18_Chapter.pdf (462.05 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01557600 , version 1 (06-07-2017)

Licence

Attribution

Identifiers

Cite

Mihaela Oprea, Sanda Florentina Mihalache, Marian Popescu. Applying Artificial Neural Networks to Short-Term PM2.5 Forecasting Modeling. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. pp.204-211, ⟨10.1007/978-3-319-44944-9_18⟩. ⟨hal-01557600⟩
85 View
128 Download

Altmetric

Share

Gmail Facebook X LinkedIn More