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Deep learning for air quality forecasts: a review
Air pollution is one of major environmental issues in the twenty-first century due to global
industrialization and urbanization. Its mitigation necessitates accurate air quality forecasts …
industrialization and urbanization. Its mitigation necessitates accurate air quality forecasts …
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 …
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 …
[HTML][HTML] A critical review of managing air pollution through airshed approach
An airshed concept has been widely practiced in developed countries as a tool for air quality
mitigation, but its application in develo** countries is still evolving. The air pollution …
mitigation, but its application in develo** countries is still evolving. The air pollution …
An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2. 5 concentration in urban environment
This study proposes a new model for the spatiotemporal prediction of PM 2.5 concentration
at hourly and daily time intervals. It has been constructed on a combination of three …
at hourly and daily time intervals. It has been constructed on a combination of three …
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 …
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 …
RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model
Predicting the concentration of air pollutants is an effective method for preventing pollution
incidents by providing an early warning of harmful substances in the air. Accurate prediction …
incidents by providing an early warning of harmful substances in the air. Accurate prediction …
Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru
The prediction of air pollution is of great importance in highly populated areas because it
directly impacts both the management of the city's economic activity and the health of its …
directly impacts both the management of the city's economic activity and the health of its …