Spatiotemporal air pollution forecasting in houston-TX: a case study for ozone using deep graph neural networks
The presence of pollutants in our atmosphere has become one of humanity's greatest
challenges. These pollutants, produced primarily by burning fossil fuels, are detrimental to …
challenges. These pollutants, produced primarily by burning fossil fuels, are detrimental to …
Graph convolutional network–Long short term memory neural network-multi layer perceptron-Gaussian progress regression model: A new deep learning model for …
M Ehteram, AN Ahmed, ZS Khozani… - Atmospheric Pollution …, 2023 - Elsevier
Ozone is one of the most important air pollutants. The high ozone concertation (OZC) affects
the environment and public health. Since OZC depends on the number of different variables …
the environment and public health. Since OZC depends on the number of different variables …
Long time series ozone prediction in China: A novel dynamic spatiotemporal deep learning approach
W Mao, L Jiao, W Wang - Building and Environment, 2022 - Elsevier
Ozone pollution is a global environmental problem becoming increasingly prominent in
China. It is of great significance to achieve long-term and high-precision ground-level ozone …
China. It is of great significance to achieve long-term and high-precision ground-level ozone …
Spatiotemporal graph convolutional recurrent neural network model for citywide air pollution forecasting
VD Le - arXiv preprint arXiv:2304.12630, 2023 - arxiv.org
Citywide Air Pollution Forecasting tries to precisely predict the air quality multiple hours
ahead for the entire city. This topic is challenged since air pollution varies in a …
ahead for the entire city. This topic is challenged since air pollution varies in a …
A city-based PM2. 5 forecasting framework using Spatially Attentive Cluster-based Graph Neural Network model
Urban environments globally are under threat due to recent climate changes caused by a
variety of factors such as growing industrialization, rapid migration, increasing traffic flow …
variety of factors such as growing industrialization, rapid migration, increasing traffic flow …
Effective PM2. 5 concentration forecasting based on multiple spatial–temporal GNN for areas without monitoring stations
IF Su, YC Chung, C Lee, PM Huang - Expert Systems with Applications, 2023 - Elsevier
With rapid industrial developments, air pollution has become a hot issue globally. Accurate
prediction of PM2. 5 (a category of particulate pollutant with a diameter of less than 2. 5 μ m) …
prediction of PM2. 5 (a category of particulate pollutant with a diameter of less than 2. 5 μ m) …
Regional prediction of ozone and fine particulate matter using diffusion convolutional recurrent neural network
Accurate air quality forecasts can provide data-driven supports for governmental
departments to control air pollution and further protect the health of residents. However …
departments to control air pollution and further protect the health of residents. However …
Deep spatio-temporal graph network with self-optimization for air quality prediction
The environment and development are major issues of general concern. After much
suffering from the harm of environmental pollution, human beings began to pay attention to …
suffering from the harm of environmental pollution, human beings began to pay attention to …
Attention enhanced hybrid model for spatiotemporal short-term forecasting of particulate matter concentrations
With ever-increasing global air pollution levels, researchers are exploring ways to forecast
air pollutant concentrations to prevent the adverse effects of air pollution on humans …
air pollutant concentrations to prevent the adverse effects of air pollution on humans …
A spatio-temporal graph convolutional network for air quality prediction
P Li, T Zhang, Y Jin - Sustainability, 2023 - mdpi.com
Air pollution is a pressing issue that poses significant threats to human health and the
ecological environment. The accurate prediction of air quality is crucial to enable …
ecological environment. The accurate prediction of air quality is crucial to enable …