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 …
Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow …
In the research, the role of three input selection techniques is evaluated on support vector
machine (SVM) performance for prediction of monthly stream flow. First, a SVM model is …
machine (SVM) performance for prediction of monthly stream flow. First, a SVM model is …
A neural network forecast for daily average PM10 concentrations in Belgium
J Hooyberghs, C Mensink, G Dumont, F Fierens… - Atmospheric …, 2005 - Elsevier
Over the past years, the health impact of airborne particulate matter (PM) has become a very
topical subject. In the environmental sciences a lot of research effort goes towards the …
topical subject. In the environmental sciences a lot of research effort goes towards the …
[HTML][HTML] Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong
J Zhang, W Ding - International journal of environmental research and …, 2017 - mdpi.com
With the development of the economy and society all over the world, most metropolitan cities
are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict …
are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict …
Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions
Air pollution is an important issue, especially in megacities across the world. There are
emission sources within and also in the regions around these cities, which cause …
emission sources within and also in the regions around these cities, which cause …
Machine learning algorithms in air quality modeling
A Masih - Global Journal of Environmental Science and …, 2019 - gjesm.net
Modern studies in the field of environment science and engineering show that deterministic
models struggle to capture the relationship between the concentration of atmospheric …
models struggle to capture the relationship between the concentration of atmospheric …
Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction
Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the
present study, four training functions, including resilient backpropagation (RP), scale …
present study, four training functions, including resilient backpropagation (RP), scale …
Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends
Monitoring and forecasting of air quality parameters are popular and important topics of
atmospheric and environmental research today due to the health impact caused by …
atmospheric and environmental research today due to the health impact caused by …
Online prediction model based on support vector machine
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 …
the development of a more accurate model is very difficult because of high non-linear and …
Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic
Predicting the stream flow is one of the most important steps in the water resources
management. Artificial neural network (ANN) has been suggested and applied for this …
management. Artificial neural network (ANN) has been suggested and applied for this …