Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5
Abstract Fine particulate matter (PM 2.5) has a considerable impact on human health, the
environment and climate change. It is estimated that with better predictions, US $9 billion …
environment and climate change. It is estimated that with better predictions, US $9 billion …
A hybrid deep learning model to forecast particulate matter concentration levels in Seoul, South Korea
G Yang, HM Lee, G Lee - Atmosphere, 2020 - mdpi.com
Both long-and short-term exposure to high concentrations of airborne particulate matter (PM)
severely affect human health. Many countries now regulate PM concentrations. Early …
severely affect human health. Many countries now regulate PM concentrations. Early …
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 …
A novel recursive model based on a convolutional long short-term memory neural network for air pollution prediction
Deep learning provides a promising approach for air pollution prediction. The existing deep
learning-based predicted models generally consider either the temporal correlations of air …
learning-based predicted models generally consider either the temporal correlations of air …
A novel deep learning model integrating CNN and GRU to predict particulate matter concentrations
Z Guo, C Yang, D Wang, H Liu - Process Safety and Environmental …, 2023 - Elsevier
PM 2.5 is a significant environmental pollutant that damages the environment and
endangers human health. Precise forecast of PM 2.5 concentrations is very important to …
endangers human health. Precise forecast of PM 2.5 concentrations is very important to …
Spatiotemporal prediction of PM2. 5 concentrations at different time granularities using IDW-BLSTM
As air pollution becomes an increasing concern globally, governments, and research
institutions have attached great importance to air quality prediction to help give early …
institutions have attached great importance to air quality prediction to help give early …
Deepairnet: Applying recurrent networks for air quality prediction
V Athira, P Geetha, R Vinayakumar… - Procedia computer science, 2018 - Elsevier
With the quick advancement of urbanization and industrialization, air pollution has become a
serious issue in developing countries. Governments and natives have raised their …
serious issue in developing countries. Governments and natives have raised their …
Spatiotemporal causal convolutional network for forecasting hourly PM2. 5 concentrations in Beijing, China
Abstract Air pollution in Northeastern Asia is a serious environmental problem, especially in
China where PM 2.5 levels are quite high. Accurate PM 2.5 predictions are significant to …
China where PM 2.5 levels are quite high. Accurate PM 2.5 predictions are significant to …
A Long Short-Term Memory (LSTM) Network for Hourly Estimation of PM2.5 Concentration in Two Cities of South Korea
Featured Application Forecasting particulate matter of size less than 2.5 µm (PM 2.5) in big
cities is a major challenge for scientific community. In addition to environmental impacts …
cities is a major challenge for scientific community. In addition to environmental impacts …
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 …