A review of artificial neural network models for ambient air pollution prediction
Research activity in the field of air pollution forecasting using artificial neural networks
(ANNs) has increased dramatically in recent years. However, the development of ANN …
(ANNs) has increased dramatically in recent years. However, the development of ANN …
Advances in air quality research–current and emerging challenges
This review provides a community's perspective on air quality research focusing mainly on
developments over the past decade. The article provides perspectives on current and future …
developments over the past decade. The article provides perspectives on current and future …
Long short-term memory-Fully connected (LSTM-FC) neural network for PM2. 5 concentration prediction
People have been suffering from air pollution for a decade in China, especially from PM 2.5
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …
Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
As air pollution becomes more and more severe, air quality prediction has become an
important approach for air pollution management and prevention. In recent years, a number …
important approach for air pollution management and prevention. In recent years, a number …
[HTML][HTML] Artificial neural networks forecasting of PM2. 5 pollution using air mass trajectory based geographic model and wavelet transformation
X Feng, Q Li, Y Zhu, J Hou, L **, J Wang - Atmospheric Environment, 2015 - Elsevier
In the paper a novel hybrid model combining air mass trajectory analysis and wavelet
transformation to improve the artificial neural network (ANN) forecast accuracy of daily …
transformation to improve the artificial neural network (ANN) forecast accuracy of daily …
Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging
NO2 is a combustion byproduct that has been associated with multiple adverse health
outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble …
outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble …
Recursive neural network model for analysis and forecast of PM10 and PM2. 5
F Biancofiore, M Busilacchio, M Verdecchia… - Atmospheric Pollution …, 2017 - Elsevier
Atmospheric particulate matter (PM) is one of the pollutant that may have a significant impact
on human health. Data collected during three years in an urban area of the Adriatic coast …
on human health. Data collected during three years in an urban area of the Adriatic coast …
A systematic review of data mining and machine learning for air pollution epidemiology
Background Data measuring airborne pollutants, public health and environmental factors
are increasingly being stored and merged. These big datasets offer great potential, but also …
are increasingly being stored and merged. These big datasets offer great potential, but also …
Spatial estimation of urban air pollution with the use of artificial neural network models
A Alimissis, K Philippopoulos, CG Tzanis… - Atmospheric …, 2018 - Elsevier
The deterioration of urban air quality is considered worldwide one of the primary
environmental issues and scientific evidence associates the exposure to ambient air …
environmental issues and scientific evidence associates the exposure to ambient air …
Methods for imputation of missing values in air quality data sets
Methods for data imputation applicable to air quality data sets were evaluated in the context
of univariate (linear, spline and nearest neighbour interpolation), multivariate (regression …
of univariate (linear, spline and nearest neighbour interpolation), multivariate (regression …