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 …
Combining chemometrics and sensors: Toward new applications in monitoring and environmental analysis
For many years, an extensive array of chemometric methods have provided a platform upon
which a quantitative description of environmental conditions can be obtained. Applying …
which a quantitative description of environmental conditions can be obtained. Applying …
Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts
Timely regional air quality forecasting in a city is crucial and beneficial for supporting
environmental management decisions as well as averting serious accidents caused by air …
environmental management decisions as well as averting serious accidents caused by air …
Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network
Q Wu, H Lin - Sustainable Cities and Society, 2019 - Elsevier
An accurate and effective air quality index (AQI) forecasting is one of the necessary
conditions for the promotion of urban public health, and to help society to be sustainable …
conditions for the promotion of urban public health, and to help society to be sustainable …
A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors
Q Wu, H Lin - Science of the Total Environment, 2019 - Elsevier
Accurate and reliable air quality index (AQI) forecasting is extremely crucial for ecological
environment and public health. A novel optimal-hybrid model, which fuses the advantage of …
environment and public health. A novel optimal-hybrid model, which fuses the advantage of …
Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization …
T Zhang, Z Tang, J Wu, X Du, K Chen - Energy, 2021 - Elsevier
The prediction of crude oil prices has important research significance. The paper contributes
to the literature of hybrid models for forecasting crude oil prices. We apply ensemble …
to the literature of hybrid models for forecasting crude oil prices. We apply ensemble …
Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance
In this study, we use a deep convolutional neural network (CNN) to develop a model that
predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 …
predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 …
Artificial neural network model for ozone concentration estimation and Monte Carlo analysis
M Gao, L Yin, J Ning - Atmospheric Environment, 2018 - Elsevier
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 …
to predict air pollutant concentrations. Air quality is a complex function of emissions …
Data multi-scale decomposition strategies for air pollution forecasting: A comprehensive review
Currently, the increasingly serious air quality has attached great significance to the
forecasting of air pollution. Data decomposition technology can decompose the original data …
forecasting of air pollution. Data decomposition technology can decompose the original data …
Short term electricity price forecasting using a new hybrid model based on two-layer decomposition technique and ensemble learning
T Zhang, Z Tang, J Wu, X Du, K Chen - Electric Power Systems Research, 2022 - Elsevier
Research on forecasting electricity prices is of great significance to market participants. It is
very difficult, however, to forecast the electricity price series because of its nonlinearity and …
very difficult, however, to forecast the electricity price series because of its nonlinearity and …