[HTML][HTML] Multi-step forecast of PM2. 5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning
Accurate and reliable forecasting of PM 2.5 and PM 10 concentrations is important to the
public to reasonably avoid air pollution and for the governmental policy responses …
public to reasonably avoid air pollution and for the governmental policy responses …
Investigation of nearby monitoring station for hourly PM2. 5 forecasting using parallel multi-input 1D-CNN-biLSTM
M Zhu, J **e - Expert Systems with Applications, 2023 - Elsevier
Air quality forecasting is a hot research topic that has been widely explored by the whole
society. To better understand environmental quality, numerous methods have been …
society. To better understand environmental quality, numerous methods have been …
A new hybrid deep neural network for multiple sites PM2. 5 forecasting
M Teng, S Li, J Yang, J Chen, C Fan, Y Ding - Journal of Cleaner …, 2024 - Elsevier
Many studies have confirmed that fine particulate matter (PM 2.5) poses significant hazards
to both human health and the ecological environment. Predicting future trends in PM 2.5 …
to both human health and the ecological environment. Predicting future trends in PM 2.5 …
Dynamic graph convolution neural network based on spatial-temporal correlation for air quality prediction
A Dun, Y Yang, F Lei - Ecological Informatics, 2022 - Elsevier
Air pollution is a serious threat to both the ecological environment and the physical health of
individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution …
individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution …
[HTML][HTML] A spatial correlation prediction model of urban PM2. 5 concentration based on deconvolution and LSTM
Precise prediction of air pollutants can effectively reducre the occurrence of heavy pollution
incidents. With the current surge of massive data, deep learning appears to be a promising …
incidents. With the current surge of massive data, deep learning appears to be a promising …
Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates …
Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality,
climate, ecosystems and human health. Therefore, measurements, prediction and …
climate, ecosystems and human health. Therefore, measurements, prediction and …
[HTML][HTML] Unmasking air quality: A novel image-based approach to align public perception with pollution levels
TC Lin, SY Wang, ZY Kung, YH Su, PT Chiueh… - Environment …, 2023 - Elsevier
In the quest to reconcile public perception of air pollution with scientific measurements, our
study introduced a pioneering method involving a gradient boost-regression tree model …
study introduced a pioneering method involving a gradient boost-regression tree model …
Improving the quantification of fine particulates (PM2. 5) concentrations in Malaysia using simplified and computationally efficient models
Air pollution assessment in urban and rural areas is really challenging due to high spatio-
temporal variability of aerosols and pollutants and the uncertainties in measurements and …
temporal variability of aerosols and pollutants and the uncertainties in measurements and …
Calibrating MERRA-2 PM2.5 concentrations with aerosol diagnostics: testing different machine learning approaches in the Eastern Mediterranean
Abstract Estimating ground-level PM2. 5 concentrations by satellite-based aerosol optical
depth (AOD) does not provide spatially and temporally continuous estimations due to the …
depth (AOD) does not provide spatially and temporally continuous estimations due to the …
Air pollutant diffusion trend prediction based on deep learning for targeted season—North China as an example
The air pollutant diffusion trend prediction plays an important role in the environment
protection. The approaches in the existing studies rarely consider spatial and temporal …
protection. The approaches in the existing studies rarely consider spatial and temporal …