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 …

Environmental monitoring is necessary to ensure the overall health and conservation of an
ecosystem. However, ecosystems (eg air, water, soil), are complex, involving numerous …

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air
pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air …

It is beneficial to establish a rapid prediction approach of sorption by considering various
operating variables that can greatly reduce workload and minimize operational costs. In the …

Ozone is one of the most important air pollutants, with significant impacts on human health,
regional air quality and ecosystems. In this study, we use geographic information and …

In this paper, we develop a method for predicting ozone (O 3) concentration based on kernel
extreme learning machine (KELM) and support vector machine regression (SVR) and …

In this paper, six types of air pollutant concentrations are taken as the research object, and
the data monitored by the micro air quality detector are calibrated by the national control …

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 …

Nowadays the use of renewable energy including wind energy has risen dramatically.
Because of the increasing development of wind power production, improvement of the …

In order to correct the monitoring data of the miniature air quality detector, an air quality
prediction model fusing Principal Component Regression (PCR), Support Vector …