PM2.5 Prediction using Deep Learning Models

Air quality monitoring and prediction are crucial in smart cities for public health, environmental sustainability, urban planning, emergency response, citizen empowerment, and data-driven decision making. By monitoring and predicting air quality, authorities can mitigate pollution sources, protect public health, and promote sustainable practices. It aids in informed urban planning, ensuring sensitive populations are not exposed to high pollution levels. Real-time monitoring enables timely responses during emergencies, protecting residents and responders. Citizens are empowered with access to accurate information, encouraging informed choices and collective action. Air quality data supports evidence-based decision making and resource allocation. Prioritizing air quality in smart cities enhances quality of life and fosters sustainable and healthy urban environments. In smart cities, we use air quality monitoring systems based on the Internet of Things (IoT) to control air pollution levels. Taking advantage of the good performance of deep neural networks in time-series prediction, these systems are upgraded to 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 the smart cities. 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 prediction results of different models in terms of prediction accuracy using the coefficient of determination (R2), mean squared error (MSE) and root mean square error (RMSE) between the actual values and the predicted values.