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 …
Multi-step ahead forecasting of daily reference evapotranspiration using deep learning
LB Ferreira, FF da Cunha - Computers and electronics in agriculture, 2020 - Elsevier
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 …
Predicting the quality of air with machine learning approaches: Current research priorities and future perspectives
The spiraling growth of the world's population and unregulated urbanization have resulted in
many environmental problems, including poor quality of air, which is associated with a wide …
many environmental problems, including poor quality of air, which is associated with a wide …
Graph convolutional network–Long short term memory neural network-multi layer perceptron-Gaussian progress regression model: A new deep learning model for …
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 …
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 …