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Dergiler / Türk Doğa ve Fen Dergisi / 2022 / Cilt: 11 - Sayı: 4

Deep Learning Based Air Quality Prediction: A Case Study for London

Anıl UTKU
Ümit CAN
Sayfa
126–134
Yayın Tarihi
2022-12-28
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The LSTM model developed according to the results obtained achieved the best result in terms of MSE, RMSE, MAE, and R2 metrics.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Although states take various measures to prevent air pollution, air pollutants continue to exist as an important problem in the world.
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Temel Bulgular & Veri:One air pollutant that seriously affects human health is called PM2.5 (particles smaller than 2.5 micrometers in diameter).
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Sonuç & Katkı:The LSTM model developed according to the results obtained achieved the best result in terms of MSE, RMSE, MAE, and R2 metrics.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Deep learning · London · PM_2.5 prediction

Abstract

Although states take various measures to prevent air pollution, air pollutants continue to exist as an important problem in the world. One air pollutant that seriously affects human health is called PM2.5 (particles smaller than 2.5 micrometers in diameter). These particles pose a serious threat to human health. For example, it can penetrate deep into the lung, irritate and erode the alveolar wall and consequently impair lung function. From this, the event PM2.5 prediction is very important. In this study, PM2.5 prediction was made using 12 models, namely, Decision Tree (DT), Extra Tree (ET), k-Nearest Neighbourhood (k-NN), Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. The LSTM model developed according to the results obtained achieved the best result in terms of MSE, RMSE, MAE, and R2 metrics.

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Bu Makaleye Atıf Yap

UTKU, A., CAN, Ü. (2022). Deep Learning Based Air Quality Prediction: A Case Study for London. Türk Doğa ve Fen Dergisi, Cilt: 11 - Sayı: 4, 126–134. https://ajindex.com/articles/865243

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Kaynakça (12)

  1. Xing YF, Xu YH, Shi MH, The impact of PM2. 5 on the human respiratory system. J. Thorac. Dis. 2016;8(1), E69. https://doi.org/ 10.3978/j.issn.2072-1439.2016.01.19Lian YX
  2. Hayes RB, Lim C, Zhang Y, Cromar K, Shao Y, Reynolds HR, et al. PM2. 5 air pollution and cause-specific cardiovascular disease mortality. Int. J. Epidemiol. 2020;49(1), 25-35
  3. He K, Yang F, Ma Y, Zhang Q, Yao X, Chan CK, et al. The characteristics of PM2. 5 in Beijing, China. Atmos. Environ. 2001; 35(29), 4959-4970.
  4. Ma J, Yu Z, Qu Y, Xu J, Cao Y. Application of the XGBoost machine learning method in PM2. 5 prediction: A case study of Shanghai. Aerosol Air Qual. Res. 2020; 20(1), 128-138.
  5. Masood A, Ahmad K. A model for particulate matter (PM2. 5) prediction for Delhi based on machine learning approaches. Procedia Comput. Sci. 2020; 167, 2101-2110.
  6. Danesh Yazdi M, Kuang Z, Dimakopoulou K, Barratt B, Suel E, Amini H, et al. Predicting fine particulate matter (PM2. 5) in the greater London area: an ensemble approach using machine learning methods. Remote Sens. 2020; 12(6), 914.
  7. Feng L, Yang T, Wang Z. Performance evaluation of photographic measurement in the machine-learning prediction of ground PM2. 5 concentrations. Atmos. Environ. 2021;262, 118623.
  8. Lv L, Wei P, Li J, Hu J. Application of machine learning algorithms to improve numerical simulation prediction of PM2. 5 and chemical components. Atmos. Pollut. Res. 2021; 12(11), 101211.
  9. Enebish T, Chau K, Jadamba B, Franklin M. Predicting ambient PM2. 5 concentrations in Ulaanbaatar, Mongolia with machine learning approaches. J. Exposure Sci. Environ. Epidemiol. 2021; 31(4), 699-708.
  10. Karimian H, Li Q, Wu C, Qi Y, Mo Y, Chen G, et al. Evaluation of different machine learning approaches to forecasting PM2. 5 mass concentrations. Aerosol Air Qual. Res. 2019; 19(6), 1400-1410.
  11. Pak U, Ma J, Ryu U, Ryom K, Juhyok U, Pak K, et al. Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Sci. Total Environ. 2020;699, 133561.
  12. Xiao Q, Chang HH, Geng G, Liu Y. An ens