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Deep learning for air pollutant concentration prediction: A review
Air pollution has become one of the critical environmental problem in the 21st century and
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
A systematic literature review of deep learning neural network for time series air quality forecasting
Rapid progress of industrial development, urbanization and traffic has caused air quality
reduction that negatively affects human health and environmental sustainability, especially …
reduction that negatively affects human health and environmental sustainability, especially …
Estimation of SPEI meteorological drought using machine learning algorithms
Accurate estimation of drought events is vital for the mitigation of their adverse
consequences on water resources, agriculture and ecosystems. Machine learning …
consequences on water resources, agriculture and ecosystems. Machine learning …
A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions
Despite the considerable reductions in primary and secondary air pollutants in China,
surface ozone levels have increased in recent years. We report a trend of 3.3±4.7 μg. m− 3 …
surface ozone levels have increased in recent years. We report a trend of 3.3±4.7 μg. m− 3 …
[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 …
Forecasting of fine particulate matter based on LSTM and optimization algorithm
Accurate air pollution forecasting may provide valuable information for urban planning to
maintain environmental sustainability and reduce mortality risk due to health problems. The …
maintain environmental sustainability and reduce mortality risk due to health problems. The …
A CNN-BiLSTM-Attention approach for EHA degradation prediction based on time-series generative adversarial network
As a representative integrated system for power-by-wire (PBW) systems, Electro-hydrostatic
actuator (EHA) has series of advantages such as high power density, compactness, and …
actuator (EHA) has series of advantages such as high power density, compactness, and …
Multi-step ahead forecasting of daily reference evapotranspiration using deep learning
Daily reference evapotranspiration (ETo) forecasts can help farmers in irrigation planning.
Therefore, this study assesses the potential of deep learning (long short-term memory …
Therefore, this study assesses the potential of deep learning (long short-term memory …
Deep Learning Estimation of Daily Ground‐Level NO2 Concentrations From Remote Sensing Data
The limited number of nitrogen dioxide (NO2) surface measurements calls for the
development of highly accurate approaches to estimating surface NO2 concentrations. In …
development of highly accurate approaches to estimating surface NO2 concentrations. In …
An air quality index prediction model based on CNN-ILSTM
J Wang, X Li, L **, J Li, Q Sun, H Wang - Scientific Reports, 2022 - nature.com
Air quality index (AQI) is an essential measure of air pollution evaluation, which describes
the air pollution degree and its impact on health, so the accurate prediction of AQI is …
the air pollution degree and its impact on health, so the accurate prediction of AQI is …