Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/200
Title: Short-term prediction of PM2.5 pollution with deep learning methods
Authors: Ayturan, Y.A.
Ayturan, Z.C.
Altun, H.O.
Kongoli, C.
Tuncez, F.D.
Dursun, S.
Öztürk, A.
Keywords: Air pollution
particulate matter
deep learning
prediction
GRU
RNN
Issue Date: 2020
Publisher: GLOBAL NETWORK ENVIRONMENTAL SCIENCE & TECHNOLOGY
Abstract: Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Stations of the Ministry of Environment and Urbanization of Turkey were utilized to obtain the data. Specifically, meteorological and air pollution data were obtained from a monitoring station located in Kecioren District of Ankara. Several trials were conducted using different combinations of RNN, GRU and LSTM models. Pollutant concentrations and meteorological factors were integrated into the model as input parameters to predict PM2.5 concentration for 1, 2 and 3 hours. Best results with R-2 of 0.83, 0.7 and 0.63 for 1, 2-, and 3-hour predictions, respectively, were obtained by using a combination of GRU and RNN models. The results of this study are promising for explaining the effect of different deep learning models on prediction performance.
URI: https://doi.org/10.30955/gnj.003208
https://hdl.handle.net/20.500.13091/200
ISSN: 1790-7632
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Files in This Item:
File SizeFormat 
gnest_03208_published.pdf619.84 kBAdobe PDFView/Open
Show full item record

CORE Recommender

SCOPUSTM   
Citations

8
checked on Feb 4, 2023

WEB OF SCIENCETM
Citations

8
checked on Jan 30, 2023

Page view(s)

246
checked on Feb 6, 2023

Download(s)

112
checked on Feb 6, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.