Deep learning models address air quality forecasting problems far more effectively and
efficiently than the traditional machine learning models. Specifically, Long Short-Term …

Air pollutant concentration forecasting is an effective method of protecting public health by
providing an early warning against harmful air pollutants. However, existing methods of air …

Accurate forecasting of air pollutant concentration is of great importance since it is an
essential part of the early warning system. However, it still remains a challenge due to the …

Entering a new era of big data, analysis of large amounts of real-time data is important, and
air quality data as streaming time series are measured by several different sensors. To this …

Air quality (mainly PM2. 5) forecasting plays an important role in the early detection and
control of air pollution. In recent times, numerous deep learning-based models have been …

In recent years, the dangers that air pollutants pose to human health and the environment
have received widespread attention. Although accurately predicting the air quality is …

Nowadays, air pollution is an important problem with negative impacts on human health and
on the environment. The air pollution forecast can provide important information to all …

Deep learning provides a promising approach for air pollution prediction. The existing deep
learning-based predicted models generally consider either the temporal correlations of air …

This paper presents one of the first applications of deep learning (DL) techniques to predict
air pollution time series. Air quality management relies extensively on time series data …

The air pollution caused by particulate matter (PM) has become a public health issue due to
the risks to human life and the environment. The PM concentration in the air causes haze …