Smart Environment Monitoring Systems for PM2.5 Prediction Using Deep Learning Models in Smart City

Abstract

Air pollution is a significant challenge for urban areas worldwide. In smart cities, we use air quality monitoring systems based on Internet of Things (IoT) to control air pollution level. Taking advantage of the good performance of deep neural networks in time-series prediction, these systems are upgradedto smart monitoring systems for improving performance and efficiency. In this work, we propose to build deep learning models such as long short-term memory (LSTM), gated recurrent unit (GRU), and hybrid convolutional neural networks-long short-term memory (CNN-LSTM) to predict air pollution in smartcities. First, we collect air data. Then, we adjust them through various preprocessing methods. Next, we deploy the different deep learning models. We train our models and test them using an air dataset. Finally, we evaluate the prediction results of different models in terms of prediction accuracy using the coefficient of determination ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> ), mean squared error (MSE), and root mean square error (RMSE) between the actual values and the predicted values.