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Journals / 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
Pages
126–134
Publication Date
2022-12-28
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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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Key Findings & Data:One air pollutant that seriously affects human health is called PM2.5 (particles smaller than 2.5 micrometers in diameter).
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Conclusion & Impact:The LSTM model developed according to the results obtained achieved the best result in terms of MSE, RMSE, MAE, and R2 metrics.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables: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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Cite This Article

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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