Air pollutant forecasting can be used to quantitatively estimate pollutant reduction trends.
Combining bibliometrics with the evolutionary tree and Markov chain methods can achieve a …

Machine learning methods originated from artificial intelligence and are now used in various
fields in environmental sciences today. This is the first single-authored textbook providing a …

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential
to predict air pollutant concentrations. Air quality is a complex function of emissions …

For time-series forecasting problems, there have been several prediction models to data, but
the development of a more accurate model is very difficult because of high non-linear and …

Multi Artificial Neural Network (ANN) models are used to forecast ozone concentration on
single-site for a better forecast accuracy in huge dataset condition. Support Vector Machine …

A combination of multivariate statistical methods, including multiple linear regression (MLR)
and feedforward backpropagation (FFBP) were used to evaluate the influence of seasons on …

The present research aims at developing an efficient and reliable module, for operational
concentration levels of particulate matter with aerodynamic diameter up to 10μm (PM10) for …

This work focuses on the prediction of hourly levels up to 8h ahead for five pollutants (SO2,
CO, NO2, NO and O3) and six locations in the area of Bilbao (Spain). To that end, 216 …

This study aims to evaluate the performance of two statistical methods, principal component
analysis and cluster analysis, for the management of air quality monitoring network of Hong …

The statistical regression and specific computational intelligence based models are
presented in this paper for the forecasting of hourly NO 2 concentrations at a historical …