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 …
Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innovation and prospects
C Liu, C ** contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019
As China is the world's largest CO 2 emitter, it is important to understand the spatio-temporal
variation of atmospheric CO 2 to reduce carbon emissions. Satellite remote sensing for …
variation of atmospheric CO 2 to reduce carbon emissions. Satellite remote sensing for …
A performance comparison study on PM2. 5 prediction at industrial areas using different training algorithms of feedforward-backpropagation neural network (FBNN)
Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM 2.5) in
the atmosphere is fast increasing in Malaysia due to industrialization and urbanization …
the atmosphere is fast increasing in Malaysia due to industrialization and urbanization …
Intraurban NO2 hotspot detection across multiple air quality products
High-resolution air quality data products have the potential to help quantify inequitable
environmental exposures over space and across time by enabling the identification of …
environmental exposures over space and across time by enabling the identification of …
[PDF][PDF] DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite
Accurate measurement of atmospheric chemicals is essential for understanding their impact
on human health, climate, and ecosystems. Satellites provide a unique advantage by …
on human health, climate, and ecosystems. Satellites provide a unique advantage by …
Erosion potential model-based ANN-MLP for the spatiotemporal modeling of soil erosion in wadi Saida watershed
Soil erosion is currently one of the most discussed natural resource degradation
phenomena in the world. The depletion of fertile soils and the degradation of terrestrial …
phenomena in the world. The depletion of fertile soils and the degradation of terrestrial …
An ensemble model-based estimation of nitrogen dioxide in a southeastern coastal region of China
S He, H Dong, Z Zhang, Y Yuan - Remote Sensing, 2022 - mdpi.com
NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious
adverse effects on the health of residents. However, the existing satellite and ground …
adverse effects on the health of residents. However, the existing satellite and ground …
Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
Effective and precise monitoring is a prerequisite to control human emissions and slow
disruptive climate change. To obtain the near-real-time status of power plant emissions, we …
disruptive climate change. To obtain the near-real-time status of power plant emissions, we …
Regression analysis of air pollution and pediatric respiratory diseases based on interpretable machine learning
Y Ji, X Zhi, Y Wu, Y Zhang, Y Yang, T Peng… - Frontiers in Earth …, 2023 - frontiersin.org
Air pollution is of high relevance to human health. In this study, multiple machine-learning
(ML) models—linear regression, random forest (RF), AdaBoost, and neural networks (NNs) …
(ML) models—linear regression, random forest (RF), AdaBoost, and neural networks (NNs) …