A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance
Accurate air quality forecasting is critical for systematic pollution control as well as public
health and wellness. Most of the traditional forecasting techniques have shown inconsistent …
health and wellness. Most of the traditional forecasting techniques have shown inconsistent …
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 …
Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation
Air pollutant concentration forecasting is an effective method of protecting public health by
providing an early warning against harmful air pollutants. However, existing methods of air …
providing an early warning against harmful air pollutants. However, existing methods of air …
A novel spatiotemporal convolutional long short-term neural network for air pollution prediction
Air pollution is a serious environmental problem that has drawn worldwide attention.
Predicting air pollution in advance has great significance on people's daily health control …
Predicting air pollution in advance has great significance on people's daily health control …
Air pollution forecasts: An overview
L Bai, J Wang, X Ma, H Lu - … journal of environmental research and public …, 2018 - mdpi.com
Air pollution is defined as a phenomenon harmful to the ecological system and the normal
conditions of human existence and development when some substances in the atmosphere …
conditions of human existence and development when some substances in the atmosphere …
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 …
Modeling air quality prediction using a deep learning approach: Method optimization and evaluation
W Mao, W Wang, L Jiao, S Zhao, A Liu - Sustainable Cities and Society, 2021 - Elsevier
Air pollution is one of the hot issues that attracted widespread attention from urban and
society management. Air quality prediction is to issue an alarm when severe pollution …
society management. Air quality prediction is to issue an alarm when severe pollution …
[HTML][HTML] Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach
In recent years, ground-level ozone has become a severe ambient pollutant in major urban
areas of China, which has adverse impacts on population health. However, in-situ …
areas of China, which has adverse impacts on population health. However, in-situ …
Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions
Air quality forecasting is an effective way to protect public health by providing an early
warning against harmful air pollutants. In this paper, a model W-BPNN using wavelet …
warning against harmful air pollutants. In this paper, a model W-BPNN using wavelet …
[图书][B] Modelling and control of dynamic systems using Gaussian process models
J Kocijan - 2016 - Springer
We are living in an era of rapidly developing technology. Dynamic systems control is not a
new methodology, but it is heavily influenced by the development of technologies for …
new methodology, but it is heavily influenced by the development of technologies for …