A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance
Accurate air quality forecasting is critical for systematic pollution control as well as public
health and wellness. Most of the traditional forecasting techniques have shown inconsistent …
health and wellness. Most of the traditional forecasting techniques have shown inconsistent …
Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming
Waste foundry sand (WFS) is a major pollutant generated from metal casting foundries and
is classified as a hazardous material due to the presence of organic and inorganic pollutants …
is classified as a hazardous material due to the presence of organic and inorganic pollutants …
PM2. 5 concentration forecasting at surface monitoring sites using GRU neural network based on empirical mode decomposition
G Huang, X Li, B Zhang, J Ren - Science of the Total Environment, 2021 - Elsevier
The main component of haze is the particulate matter (PM) 2.5. How to explore the laws of
PM2. 5 concentration changes is the main content of air quality prediction. Combining the …
PM2. 5 concentration changes is the main content of air quality prediction. Combining the …
Tackling environmental challenges in pollution controls using artificial intelligence: A review
This review presents the developments in artificial intelligence technologies for
environmental pollution controls. A number of AI approaches, which start with the reliable …
environmental pollution controls. A number of AI approaches, which start with the reliable …
Artificial intelligence technologies for forecasting air pollution and human health: a narrative review
Air pollution is a major issue all over the world because of its impacts on the environment
and human beings. The present review discussed the sources and impacts of pollutants on …
and human beings. The present review discussed the sources and impacts of pollutants on …
A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration
Urban air pollutant concentration prediction is dealing with a surge of massive
environmental monitoring data and complex changes in air pollutants. This requires effective …
environmental monitoring data and complex changes in air pollutants. This requires effective …
Machine learning-based prediction of air quality
YC Liang, Y Maimury, AHL Chen, JRC Juarez - applied sciences, 2020 - mdpi.com
Air, an essential natural resource, has been compromised in terms of quality by economic
activities. Considerable research has been devoted to predicting instances of poor air …
activities. Considerable research has been devoted to predicting instances of poor air …
Machine learning and statistical models for predicting indoor air quality
Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be
predicted using either physically based mechanistic models or statistical models that are …
predicted using either physically based mechanistic models or statistical models that are …
[HTML][HTML] Multi-step forecast of PM2. 5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning
Accurate and reliable forecasting of PM 2.5 and PM 10 concentrations is important to the
public to reasonably avoid air pollution and for the governmental policy responses …
public to reasonably avoid air pollution and for the governmental policy responses …
Air quality prediction using CNN+ LSTM-based hybrid deep learning architecture
Air pollution prediction based on variables in environmental monitoring data gains further
importance with increasing concerns about climate change and the sustainability of cities …
importance with increasing concerns about climate change and the sustainability of cities …