Accurate prediction of PM2. 5 concentrations can provide a solid foundation for preventing
and controlling air pollution. When the Long Short-Term Memory (LSTM) is applied to predict …

Air pollution, especially by particulate matter with diameters less than 2.5 μm (PM2. 5), is a
serious threat to public health. The accurate prediction of PM2. 5 concentration is significant …

Abstract Machine learning has become a powerful tool in air quality assessment which can
provide timely and predictable information, alert the public, and take timely measures to …

Many cities have long suffered from the events of fine particulate matter (PM 2.5) pollutions.
The Taiwanese Government has long strived to accurately predict the short-term hourly …

Air pollution poses a serious threat to human health and the environment worldwide, of
which particulate matter (PM2. 5), receives an increasing attention with deeper recognition …

Deep learning algorithms have been effective in predicting PM2. 5. A deep learning
algorithm integrating the convolutional neural networks (CNNs) and LSTM networks is …

Over the past few decades, air pollution has caused serious damage to public health.
Therefore, making accurate predictions of PM2. 5 is a crucial task. Due to the transportation …

Accurately predicting PM2. 5 concentration can effectively avoid the harm caused by heavy
pollution weather to human health. In view of the non-linearity, time series characteristics …

Abstract Beijing, Shanghai, Singapore, and London are regions with high population density
and industrial activities. In this sense, accurate prediction of the rate of particulate matter 2.5 …

PM2. 5 is one of the most important pollutants related to air quality, and the increase of its
concentration will aggravate the threat to people's health. Therefore, the prediction of …