Short-Term Prediction of PM2. 5 Pollution with Deep Learning Methods

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.

Erişime Açık
Görüntülenme
6
22.03.2024 tarihinden bu yana
İndirme
1
22.03.2024 tarihinden bu yana
Son Erişim Tarihi
20 Nisan 2024 01:05
Google Kontrol
Tıklayınız
Tam Metin
Tam Metin İndirmek için tıklayın Ön izleme
Detaylı Görünüm
Eser Adı
(dc.title)
Short-Term Prediction of PM2. 5 Pollution with Deep Learning Methods
Yayın Türü
(dc.type)
Makale
Yazar/lar
(dc.contributor.author)
AYTURAN, Yasin Akın
Yazar/lar
(dc.contributor.author)
AYTURAN, Zeynep Cansu
Yazar/lar
(dc.contributor.author)
ALTUN, Hüseyin Oktay
Yazar/lar
(dc.contributor.author)
KONGOLİ, Cezar
Yazar/lar
(dc.contributor.author)
TUNÇEZ, Fatma Didem
Yazar/lar
(dc.contributor.author)
DURSUN, Şükrü
Yazar/lar
(dc.contributor.author)
ÖZTÜRK, Ali
Atıf Dizini
(dc.source.database)
Wos
Atıf Dizini
(dc.source.database)
Scopus
Konu Başlıkları
(dc.subject)
Prediction
Konu Başlıkları
(dc.subject)
Air Pollution
Konu Başlıkları
(dc.subject)
Particulate Matter
Konu Başlıkları
(dc.subject)
Deep Learning
Konu Başlıkları
(dc.subject)
RNN
Konu Başlıkları
(dc.subject)
GRU
Yayıncı
(dc.publisher)
Global Nest Journal-GLOBAL NETWORK ENVIRONMENTAL SCIENCE & TECHNOLOGY
Yayın Tarihi
(dc.date.issued)
2020
Kayıt Giriş Tarihi
(dc.date.accessioned)
2023-03-02T20:39:10Z
Açık Erişim tarihi
(dc.date.available)
2023-03-02T20:39:10Z
ISSN
(dc.identifier.issn)
1790-7632
Özet
(dc.description.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.
Yayın Dili
(dc.language.iso)
en
Seri Adı ve Numarası
(dc.relation.ispartofseries)
22/1;
Tek Biçim Adres
(dc.identifier.uri)
http://hdl.handle.net/20.500.12498/5939
Analizler
Yayın Görüntülenme
Yayın Görüntülenme
Erişilen ülkeler
Erişilen şehirler
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve cerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms