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

Authors : Anıl UTKU, Ümit CAN

Pages : 126-134

Doi:10.46810/tdfd.1201415

View : 59 | Download : 8

Publication Date : 2022-12-28

Article Type : Research Paper

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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(DT);, Extra Tree insert ignore into journalissuearticles values(ET);, k-Nearest Neighbourhood insert ignore into journalissuearticles values(k-NN);, Linear Regression insert ignore into journalissuearticles values(LR);, Random Forest insert ignore into journalissuearticles values(RF);, Support Vector Machine insert ignore into journalissuearticles values(SVM);, Extreme Gradient Boosting insert ignore into journalissuearticles values(XGBoost);, Multi-Layer Perceptron insert ignore into journalissuearticles values(MLP);, Convolutional Neural Network insert ignore into journalissuearticles values(CNN);, Recurrent Neural Network insert ignore into journalissuearticles values(RNN);, Gated Recurrent Unit insert ignore into journalissuearticles values(GRU);, and Long Short-Term Memory insert ignore into journalissuearticles values(LSTM); models. The LSTM model developed according to the results obtained achieved the best result in terms of MSE, RMSE, MAE, and R2 metrics.
Keywords : Deep learning, London, PM 2 5 prediction, Machine learning, Air quality prediction

ORIGINAL ARTICLE URL