Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is
important to develop forecasting mechanisms that can be used by the authorities, so that …

Rapid industrialization and urbanization are rapidly deteriorating ambient air quality,
especially in the developing nations. Air pollutants impose a high risk on human health and …

As a significant issue in the machine learning field, the long-term forecasting of time series
has aroused extensive attention from academia and industry. Specifically, transforming time …

Considerable research achievements have been made in utilizing information granulation
as an effective technique for addressing long-term time-series forecasting. However, existing …

Accurate air quality prediction can help cope with air pollution and improve the life quality.
With the development of the deployments of low-cost air quality sensors, increasing data …

Although many machine learning methods have been widely used to predict PM2. 5
concentrations, these single or hybrid methods still have some shortcomings. This study …

Predicting the concentration of PM 2.5 particles is of critical importance in public health
management because their small size enables them to penetrate deep into the lungs and …

This article presents a location-invariant air pollution prediction model with good geographic
generalizability. The model uses a light GBR as part of a machine-learning framework to …

Air pollution poses a grave threat to human health and everyday life. Accurate air-quality
prediction plays a crucial role in effectively preventing and controlling air pollution. A multi …

With rising environmental concerns, accurate air quality predictions have become
paramount as they help in planning preventive measures and policies for potential health …