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 …
Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts
Timely regional air quality forecasting in a city is crucial and beneficial for supporting
environmental management decisions as well as averting serious accidents caused by air …
environmental management decisions as well as averting serious accidents caused by air …
Application of wavelet-packet transform driven deep learning method in PM2. 5 concentration prediction: A case study of Qingdao, China
Q Zheng, X Tian, Z Yu, N Jiang, A Elhanashi… - Sustainable Cities and …, 2023 - Elsevier
Air pollution is one of the most serious environmental problems faced by human beings, and
it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction …
it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction …
PM2. 5 concentrations forecasting in Beijing through deep learning with different inputs, model structures and forecast time
J Yang, R Yan, M Nong, J Liao, F Li, W Sun - Atmospheric Pollution …, 2021 - Elsevier
Timely and accurate air quality forecasting is of great significance for prevention and
mitigation of air pollution. However, most of the previous forecasting models only considered …
mitigation of air pollution. However, most of the previous forecasting models only considered …
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 …
[HTML][HTML] Air-pollution prediction in smart city, deep learning approach
Over the past few decades, due to human activities, industrialization, and urbanization, air
pollution has become a life-threatening factor in many countries around the world. Among …
pollution has become a life-threatening factor in many countries around the world. Among …
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 …
A Lag-FLSTM deep learning network based on Bayesian Optimization for multi-sequential-variant PM2. 5 prediction
To better support the prevention of air pollutions for sustainable cities, researchers have
studied different methods to forecast air pollutant concentrations. Existing methods have …
studied different methods to forecast air pollutant concentrations. Existing methods have …
A hybrid CNN-LSTM model for forecasting particulate matter (PM2. 5)
T Li, M Hua, XU Wu - Ieee Access, 2020 - ieeexplore.ieee.org
PM2. 5 is one of the most important pollutants related to air quality, and the increase of its
concentration will aggravate the threat to people's health. Therefore, the prediction of …
concentration will aggravate the threat to people's health. Therefore, the prediction of …
A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration
Urban air pollutant concentration prediction is dealing with a surge of massive
environmental monitoring data and complex changes in air pollutants. This requires effective …
environmental monitoring data and complex changes in air pollutants. This requires effective …