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 …
Autoregressive models in environmental forecasting time series: a theoretical and application review
Though globalization, industrialization, and urbanization have escalated the economic
growth of nations, these activities have played foul on the environment. Better understanding …
growth of nations, these activities have played foul on the environment. Better understanding …
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 …
[HTML][HTML] DeepAR: Probabilistic forecasting with autoregressive recurrent networks
Probabilistic forecasting, ie, estimating a time series' future probability distribution given its
past, is a key enabler for optimizing business processes. In retail businesses, for example …
past, is a key enabler for optimizing business processes. In retail businesses, for example …
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 …
Deep air quality forecasting using hybrid deep learning framework
Air quality forecasting has been regarded as the key problem of air pollution early warning
and control management. In this article, we propose a novel deep learning model for air …
and control management. In this article, we propose a novel deep learning model for air …
[HTML][HTML] Artificial neural networks forecasting of PM2. 5 pollution using air mass trajectory based geographic model and wavelet transformation
X Feng, Q Li, Y Zhu, J Hou, L Jin, J Wang - Atmospheric Environment, 2015 - Elsevier
In the paper a novel hybrid model combining air mass trajectory analysis and wavelet
transformation to improve the artificial neural network (ANN) forecast accuracy of daily …
transformation to improve the artificial neural network (ANN) forecast accuracy of daily …
Hybrid structures in time series modeling and forecasting: A review
Z Hajirahimi, M Khashei - Engineering Applications of Artificial Intelligence, 2019 - Elsevier
The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies
of single models in processing various patterns and relationships latent in data, hybrid …
of single models in processing various patterns and relationships latent in data, hybrid …
Deep learning architecture for air quality predictions
With the rapid development of urbanization and industrialization, many developing countries
are suffering from heavy air pollution. Governments and citizens have expressed increasing …
are suffering from heavy air pollution. Governments and citizens have expressed increasing …