Intelligent modeling strategies for forecasting air quality time series: A review
In recent years, the deterioration of air quality, the frequent events of the air contaminants,
and the health impacts from that have caused continuous attention by the government and …
and the health impacts from that have caused continuous attention by the government and …
Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis
Artificial intelligence (AI) techniques have substantially changed the research paradigm in
the field of air quality forecasting due to their powerful performance. Considering the …
the field of air quality forecasting due to their powerful performance. Considering the …
A machine learning approach to predict air quality in California
Predicting air quality is a complex task due to the dynamic nature, volatility, and high
variability in time and space of pollutants and particulates. At the same time, being able to …
variability in time and space of pollutants and particulates. At the same time, being able to …
Forecasting air quality time series using deep learning
This paper presents one of the first applications of deep learning (DL) techniques to predict
air pollution time series. Air quality management relies extensively on time series data …
air pollution time series. Air quality management relies extensively on time series data …
Constructing a PM2. 5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks
Air pollution problems have a severe effect on the natural environment and public health.
The application of machine learning to air pollutant data can result in a better understanding …
The application of machine learning to air pollutant data can result in a better understanding …
Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors
The factors affecting the shear strength of the angle shear connectors in the steel-concrete
composite beams can play an important role to estimate the efficacy of a composite beam …
composite beams can play an important role to estimate the efficacy of a composite beam …
A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2. 5 concentration forecasting
M Niu, Y Wang, S Sun, Y Li - Atmospheric environment, 2016 - Elsevier
To enhance prediction reliability and accuracy, a hybrid model based on the promising
principle of “decomposition and ensemble” and a recently proposed meta-heuristic called …
principle of “decomposition and ensemble” and a recently proposed meta-heuristic called …
Greek long-term energy consumption prediction using artificial neural networks
L Ekonomou - Energy, 2010 - Elsevier
In this paper artificial neural networks (ANN) are addressed in order the Greek long-term
energy consumption to be predicted. The multilayer perceptron model (MLP) has been used …
energy consumption to be predicted. The multilayer perceptron model (MLP) has been used …
Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning
Estimating population exposure to particulate matter during wildfires can be difficult because
of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes …
of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes …
A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation
K Mohammadi, S Shamshirband, CW Tong… - Energy Conversion and …, 2015 - Elsevier
In this paper, a new hybrid approach by combining the Support Vector Machine (SVM) with
Wavelet Transform (WT) algorithm is developed to predict horizontal global solar radiation …
Wavelet Transform (WT) algorithm is developed to predict horizontal global solar radiation …